model-based clustering for categorical and mixed data sets

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HAL Id: tel-01076418 https://tel.archives-ouvertes.fr/tel-01076418 Submitted on 22 Oct 2014 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Model-based clustering for categorical and mixed data sets Matthieu Marbac-Lourdelle To cite this version: Matthieu Marbac-Lourdelle. Model-based clustering for categorical and mixed data sets. Statistics [math.ST]. université lille 1, 2014. English. tel-01076418

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Page 1: Model-based clustering for categorical and mixed data sets

HAL Id: tel-01076418https://tel.archives-ouvertes.fr/tel-01076418

Submitted on 22 Oct 2014

HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.

L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.

Model-based clustering for categorical and mixed datasets

Matthieu Marbac-Lourdelle

To cite this version:Matthieu Marbac-Lourdelle. Model-based clustering for categorical and mixed data sets. Statistics[math.ST]. université lille 1, 2014. English. tel-01076418

Page 2: Model-based clustering for categorical and mixed data sets

♥♠ér♦ ♦rr

❯♥rsté ♦rt♦r P P♥é

ès

♣rés♥té ♣♦r ♦t♥t♦♥

♣ô♠ ♦t♦rt

♥rsté

♣été té♠tqs

♦ès ♠é♥ ♣♦r sst♦♥♥♦♥ s♣rsé ♦♥♥és qtts t

♠①ts

♣r

tt r♦r

♦t♥ s♣t♠r ♥t r② ♦♠♣♦sé

♠trs rs Pr♦ssr ❯♥rst② ♦ t♥s ♣♣♦rtr♥ r♥ Pr♦ssr ❯♥rsté ♦♥t♣r ♣♣♦rtrs ① rtr r ♥r ② ①♠♥tr♦s ❲r Pr♦ssr ❯♥rsté ①♠♥trrst♦♣ r♥ Pr♦ssr ❯♥rsté rtr❱♥♥t ❱♥ Pr♦ssr ❯♥rsté rtr

Page 3: Model-based clustering for categorical and mixed data sets

ès ♣ré♣ré é♣rt♠♥t té♠tqs♦rt♦r P P♥é ❯ ❯♥rsté ❱♥ sq ❳

Page 4: Model-based clustering for categorical and mixed data sets

♠r♠♥ts

rst ♦ s♥r② ♦ t♦ t♥ ♠trs rs ♥ ♥ r♥♦r ♣t♥ t♦ r♣♦rt ♦♥ ts tss s s s ① ♥ ♦s ❲r♦r tr ♥ ♣rt♣t♦♥ s ①♠♥rs ♥ t P ♥s

rss é♠♥t ♣r♦♦♥s r♠r♠♥ts à ♠s ① rtrs tèsrst♦♣ r♥ t ❱♥♥t ❱♥ ♣♦r r ♥r♠♥t ♦rs s tr♦s ♥♥és ❱♦s ③ s str ♥♦♠r① ♦♥ss t♦t ♥ ♠ ss♥t♥ rté q♥t ① ①s rrs q ♣ ①♣♦rr q ♦rt♠♥t♣♣réé ♦s r♠r ♠♦r ♦♥♥é ♦♣♣♦rt♥té ♠♥tr à rr♦rs ♠♦♥ st ♠str t ♦s êtr é♠♥és ♣♦r ♠ tr♦r ♥ ♥♥♠♥t tès

r♠r r♥ q ♠ ♦♥♥é ♦ût à rr ♦rs ♥♦s ré♥♦♥s q s s♦♥t éés ss ♣rtèr♠♥t r♦♥♥ss♥t ♥rs r♥ r♠♥♥ q ♠ ♥♦ré ♣s ♠♦♥ ♠str t q t♦♦rs s♣r♥r t♠♣s ♠ ♣r♦r ♣ré① ♦♥ss

rss é♠♥t ♠s r♠r♠♥ts à ♥s♠ éq♣ ♦ù ♠♦♥♥tért♦♥ été té râ à ♥r♥ ♥

♦rs s tr♦s ♥♥és tès ♣sr é♦rr ♠♦♥ ♥s♥♠♥t r♥ ♠r rst♥ Pr t ♥ qs

r♠r ♣rs♦♥♥ ♦rt♦r P♥é ♥ ♣rtr ♥ r♥t② éè♥ réérq ♥qs r♥ç♦s t♦♠t t ♠r r♦ ♠s

P♥♥t tt tès ♣sr é♥r ♥♦♠rss ♣rs♦♥♥s♣r♠ sqs r Pr♠t ①♥r ♥♠♥ Prr♦s t P♦rt t ♥ s ♠s ♦♥t été ♣rtèr♠♥t ♠é♠♦rs râ à ♥t♦♥ ♠ t Prr r à ♦ t s ♥ ♣s ♠s ♥t ♣♦r ss♦♥ss ♥ ♣rtr ♣♥♥t ♠ rét♦♥

r♠r s ♠♠rs ♠ ♠ qs ♠♥t ♦ ♥♦♥ s ♥♥s ♣♦rr s♦t♥ t ♣r q ♥ ss sr s ♦♥t t♦s rt♥ ttr ♠ tèsrss ♥ ♠♥t♦♥ s♣é ♣♦r é♦ q ♠ s♣♣♦rt q♦t♥

♥♥ ♣rés♥t ♠s ①ss à ① q ♦♥ts♠♥t ♦é ♠♥t♦♥♥rt q s♣èr ♥ ♠♥ t♥r♦♥s ♣s rr

Page 5: Model-based clustering for categorical and mixed data sets
Page 6: Model-based clustering for categorical and mixed data sets

♦♥t♥ts

♠r♠♥ts

♦r♦r

♥ rt♦♥s ♥ ♥♦tt♦♥s

str ♥②ss stt ♦ t rt r ♦ t str♥ ♣♣r♦s ♥rts ♦♥ ♥t ♠①tr ♠♦s Pr♠tr st♠t♦♥ ♦ st♦♥ ♦♥s♦♥

♦s str♥ ♦r t♦r t

str ♥②ss ♦ t♦r t sts stt ♦ t rt ♥ ♦ str ♥②ss ♦r t♦r t ♦♠tr ♣♣r♦s ♦♥r ♠①tr ♠♦s ①trs ♦ trs t t♥t ss ♠♦ ♦♥s♦♥

♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s ♥tr♦t♦♥ ①tr ♦ ♥trss ♥♣♥♥t ♦s Prs♠♦♥♦s ♦ strt♦♥ ①♠♠ ♦♦ st♠t♦♥ ♦rt♠ ♦ st♦♥ ♦rt♠ ♠r ①♣r♠♥ts ♦♥ s♠t t sts ♥②ss ♦ t♦ r t sts ♦♥s♦♥

♦s str♥ t ♦♥t♦♥ ♣♥♥② ♠♦s ♥tr♦t♦♥ ①tr ♠♦ ♦ ♠t♥♦♠ strt♦♥s ♣r ♠♦s

Page 7: Model-based clustering for categorical and mixed data sets

♦♥t♥ts

①♠♠ ♦♦ st♠t♦♥ ♥ ♦rt♠ ♦ st♦♥ tr♦♣♦st♥s s♠♣r ♠r ①♣r♠♥ts ♦♥ s♠t t sts ♥②ss ♦ t♦ r t sts ♦♥s♦♥

♦ ♦♠♣rs♦♥ ♣r♦r♠ ② tr ♣s strt ♦♦s

♦♥s♦♥ ♦ Prt

♦s str♥ ♦r ♠① t

str ♥②ss ♦ ♠① t sts stt ♦ t rt ♥ ♦ str ♥②ss ♦r ♠① t r ♦ s♠♣ ♠t♦s t♦ str ♠① t ①tr ♦ ♦t♦♥ ♠♦s ♥ ts ①t♥s♦♥ ♣r ♦s ❯♥r♥ ss♥ ♠①tr ♠♦ ♦♥s♦♥

♦s str♥ ♦ ss♥ ♥ ♦st strt♦♥s ♥tr♦t♦♥ ①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s ①♠♠ ♦♦ st♠t♦♥ ♥ ♦rt♠ ♦ st♦♥ ♦rt♠ ♠r ①♣r♠♥ts ♦♥ s♠t t sts ♥②ss ♦ t♦ r t sts ♦♥s♦♥

♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t ♥tr♦t♦♥ ①tr ♠♦ ♦ ss♥ ♦♣s ②s♥ ♥r♥ tr♦♣♦st♥s s♠♣r ♠r ①♣r♠♥ts ♦♥ s♠t t sts ♥②ss ♦ tr r t sts ♦♥s♦♥

♦♥s♦♥ ♦ Prt

♥r ♦♥s♦♥ ♥ ♣rs♣ts

♣♣♥① ♦ Prt ♥r ♥tt② ♦ t ♠①tr ♦ t t♦ ①tr♠ ♣♥♥②

strt♦♥s

Page 8: Model-based clustering for categorical and mixed data sets

♦♥t♥ts

♥r ♥tt② ♦ t ♠①tr ♠♦ ♦ ♠t♥♦♠ strt♦♥s ♣r ♠♦s

♦♠♣tt♦♥ ♦ t ♥trt ♦♠♣tt ♦♦ ♦ t ♠①tr♠♦ ♦ ♠t♥♦♠ strt♦♥s ♣r ♠♦s

♣♣♥① ♦ Prt ♥tt② ♦ t ♠①tr ♠♦ ♦ ss♥ ♥ ♦st str

t♦♥s ♥tt② ♦ t ♠①tr ♠♦ ♦ ss♥ ♦♣s

♦r♣②

Page 9: Model-based clustering for categorical and mixed data sets
Page 10: Model-based clustering for categorical and mixed data sets

♦r♦r

♥ t ♥ t♦ str

t qst♦♥ s ♦♠ ♥rs♥② s② t♥s t♦ t ♥rs ♣r♦r♠♥ ♦ ♦♠♣t♥ r♦r ♣rtt♦♥rs r ♥ t sts r ♥rs♥② ♥♦r♠t♦♥r t s♦ ♥rs♥② strs ♥ t ♥♦r♠t♦♥♦♥t♥ ♥ t t sts ♥ rt② ♥tt♥ ♦r t♦ ♠♥ rs♦♥s tq♥tt② ♦ t ♥ tr ♦♠♣①t② s sttst ♠t♦s r ♠♥t♦r② t♦♥②③ s t sts

str♥ s ♥ ♣♣r♦ rs t ♣r♦♠ s ② t r q♥tt②♦ t ♥ ts ♠ s t♦ r♦♣ t ♥s ♥t♦ s♣ sss st ♣r♦s ♠♥♥ s♠♠r② ♦ t t st tr♦♦t rtrst♥s ♦ t ♥ ♦r ♦t str♥ s ♥tr ♦r ♥st♥t ♥ ♦r s ♥t♦ t♦ sss ♣♥ts ♥ ♥♠s t ♥♠s rs♣t ♥t♦ ♥rtrt ♥ rtrt t rtrt r ss ♥t♦ sss♠♠♠s ss rs ♠♣♥s r♣ts

♣r♦st ♠t♦s ♣r♠t t♦ ♣r♦r♠ t str ♥②ss ♥ r♦r♦s♦♥t①t ♠♦♥ ts ♠t♦s t ♥t ♠①trs ♦ ♣r♠tr strt♦♥s s♠♠r③ t t ② t ♣r♠trs ♦ ss ♦r♦r ♥ ts ♦♥t①t tss ♣r♦st t♦♦s r t♦ ♥sr t t qst♦♥s ♦ str♥②ss t ♦ ♦ t ♥♠r ♦ sss t ♦r♣② s ♣r♦ ♦t♦♥t♥♦s t sts ♥♦t s♦rt ♥ t t r ♠♦r ♦♠♣① ♥ ts♦♥t①t t ♠ ♦ ts ♠♥sr♣t s t♦ st② ①st♥ ♣r♦st ♠t♦s ♥t♦ ♣r♦♣♦s ♥ ♦♥s t♦ str ♦♠♣① t sts

t♦ ♦ts ♦ ts ♦r

❲ ♦s ♦♥ t♦ stt♦♥s r t t sts r ♦♠♣① t s r ♥s r sr ② t♦r rs ♥ t s r t② r sr② ♠① rs r♥t ♥s ♦ rs s t♦ t♠ts r st

♠♦s str♥ ♦r t♦r t sts ♠♦s str♥ ♦r ♠① t sts

t♦r rs r t t♦ str s♥ t② t sttst♥♥ t ♠♥② ♦♠♥t♦r ♥s s t② ♥rss ♥ t rs r ♣♥♥t ♥ t s♠ ss ♥ t ♠♦s rqr r ♥♠r ♦♣r♠trs ♥ ♦rr t♦ t ♥t♦ ♦♥t t ♥trss ♣♥♥s s t ss ♣♣r♦ ss♠s t ♦♥t♦♥ ♥♣♥♥ t♥ rs ♦r

Page 11: Model-based clustering for categorical and mixed data sets

♦r♦r

ts ♣♣r♦ s s ♥ t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ s ♦t♥② tr♥t ♣♣r♦s ♥ ♣r♦♣♦s t tr ♥srs st② ♥♦♠♣t♥ t ♦♠♥t♦r ♣r♦♠ ♦ t ♠♦ st♦♥ s ♥♦t ②s s♦ ♦r♦r ts ♠t♦s ♥ sr r♦♠ ♥stt② ♦r r♦♠ ♦ ♥tr♣rtt② ♥ts ♦♥t①t ♦r ♦♥trt♦♥ ♦♥ssts ♥ t♦ ♣rs♠♦♥♦s ♠①tr ♠♦s ♦ t♦ str t♦r t ♣rs♥t♥ ♥trss ♣♥♥s ♠♥ ♦ ts ♠♦s s t♦ r♦♣ t rs ♥t♦ ♦♥t♦♥② ♥♣♥♥t ♦s ②stt♥ s♣ strt♦♥s ♦r ts ♦s ♦t ♠♦s ♦♥sr t ♥trss♣♥♥s t♥ t rs ♦t ♠♦s ♣r♦ ♣r♠trs t♦ s♠♠r③ t t ♣r♦♣♦s r♦r♦s ♣♣r♦ t♦ ♣r♦r♠ t ♠♦ st♦♥ ♥ srr♥② s③t♦♥ ♦ t ♣r♠trs ♥ ♦ t t♦rt str♥ s ♠♦tt ② t t tt t♦r t r s② sss t② r ♥♠r♦s t rs t♦ ♦sr ♥trss ♦rrt t ♥rss

st② ♦ t str ♥②ss ♦ ♠① t sts s t s♦♥ ♦t ♦ts ♦r s ♣r♦♠ s ♠♦tt ② t t tt t rr♥t t sts r ♦t♥♦♠♣♦s t r♥t ♥s ♦ t ss ♣♣r♦ ss t♦r ♥②③rs♠t♦s ♥tr♣rtt♦♥ ♦ s ♠t♦ s ♦t♥ ♦♠♣① s♥ t ♣r♠trsr ♥♦t rt t♦ t rs ♥ tr ♥t s♣ tr ss ♣♣r♦s♦♥sst ♥ ♣♣②♥ s♣ ♠①tr ♠♦ ♦♥ ts t ♥ s t♦t ♦ ss strt♦♥s ♦r ♠① rs ❲ ♣r♦♣♦s t♦ ♠①tr ♠♦s t♦ ts ♣ rst ♦♥ s ss s♥ t s ♥ ①t♥s♦♥ ♦ ♥♦♥♠t♦ t♦ str t♦r t sts ♥ t ♠♦ ♦♠♥s ss♥ strt♦♥s ♥ ♥r ♦st rrss♦♥s s ts ♠♦ ♥②③s t sts t♦♥t♥♦s ♥ t♦r rs s♦♥ ♠♦ s t ♠♥ ♦♥trt♦♥ ♦ts tss s♥ t ♦s t♦ ♥②③ t sts t ♥② ♥ ♦ rs ♠tt♥ ♠t strt♦♥ ♥t♦♥ s ♠♦ s ♥ s ♠①tr ♦ ss♥ ♦♣s ts ♣rsr♥ ss ♦♥♠♥s♦♥ ♠r♥ strt♦♥ ♦r rs ♦ ♦♠♣♦♥♥t rtr♠♦r ts ♣♣r♦ ♠♦③s t ♥trss♣♥♥s ♥② ♥♦t tt s③t♦♥ t♦♦ s s ②♣r♦t ♦ts ♠♦

r♥③t♦♥ ♦ t ♠♥sr♣t

♠♥sr♣t s ♥t♦ t♦ ♠♥ ♣rts ♦rrs♣♦♥♥ t♦ t t♦ ♥s♦ t ♦ ♥trst ♦r ♣rs② t s ♦r♥③ s ♦♦s

♣tr s r ♦r ♦ t ♠♥ str♥ ♠t♦s ♥ ♥rr♠♦r t ♦ss ♦♥ t r♥t s♣ts rt t♦ ♥t ♠①tr ♠♦s t ♥tr♦s t ♥r ♥♦t♦♥s ♥ ♦rt♠s s ♥ t ♦♦♥♣trs

Prt ♦s str♥ ♦r t♦r t

♣tr ♦♥ssts ♥ t stt ♦ t rt ♦ t ♠t♦s ♣r♦r♠♥ tstr ♥②ss ♦ t♦r t sts

Page 12: Model-based clustering for categorical and mixed data sets

♦r♦r

♣tr ♣rs♥ts ♦r rst ♦♥trt♦♥ t♦ t t♦r t ♥②ssr♠♦r ♣r♦♣♦s ♠♦ r♦♣s t rs ♥t♦ ♦♥t♦♥②♥♣♥♥t ♦s s♣ strt♦♥ ♦ t ♦s ♠♦③s t♥trss ♣♥♥s ♥ ♣r♦s s♣ ♦♥t s♠♠r③♥t str♥t ♦ ts ♣♥♥s ts rsts r ♣rt ♦ t rt ♦s str♥ ♦r ♦♥t♦♥② ♦rrt t♦r t❬❱❪

♣tr ♣rs♥ts ♦r s♦♥ ♦♥trt♦♥ t♦ t t♦r t ♥②ss r♠♦r s ♠♦ ♦♥ssts ♥ ♠①tr ♠♦ r♦♣st rs ♥t♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♦ ♦♦s ♣rs♠♦♥♦s ♠t♥♦♠ strt♦♥ r t r ♣r♠trs♦rrs♣♦♥ t♦ ts ♠♦s ts rsts r ♣rt ♦ t rt ♥t♠①tr ♠♦ ♦ ♦♥t♦♥ ♣♥♥s ♠♦s t♦ str t♦rt ❬❱❪

♣tr strts ♦t ♣s ♣r♦r♠ t ♥r♥ ♦♦t ♣r♦♣♦s ♠♦s s ♣tr ♥ s♦ s s tt♦r ♦♦t ♣s s♥ t ♣r♦s ♣rs♥tt♦♥ ♦ tr ♠♥ ♥t♦♥s ♥♠♥② sr♣ts ♦♥ t♦ ♣r♦r♠ t str ♥②ss

Prt ♦s str♥ ♦r ♠① t

♣tr ♦♥ssts ♥ t stt ♦ t rt ♦ t ♠t♦s ♣r♦r♠♥ tstr ♥②ss ♦ ♠① t sts

♣tr ♣rs♥ts ♦r rst ♦♥trt♦♥ t♦ t str ♥②ss r♠♦r ♦ ♠① t sts t ♦♥t♥♦s ♥ t♦r rs ♠♦ s r r♦♠ t ♠t t♥t ss ♠♦ ♦♣ t♦ str t♦r t sts ♦r ts ♠♦ t ♦♠♣♦♥♥t strt♦♥s ♦t ♦♥t♥♦s rs r ss♥ ♥ t♦s ♦ t t♦r rs ♦♥t♦♥② ♦♥ t ♦♥t♥♦s ♦♥s r ♥r ♦st rrss♦♥s ♠♦ st♦♥ ♥ t ♣r♠tr st♠t♦♥ r s♠t♥♦s② ♣r♦r♠ ② ♠ ♦rt♠

♣tr ♣rs♥ts t ♠♥ ♦♥trt♦♥ ♦ ts tss t ♦♥ssts ♥ ♠①tr ♠♦ ♦ ss♥ ♦♣s s t ♠♦ ♣r♦r♠s t str ♥②ss ♦ t sts ♦♠♣♦s ♦ ♥② ♥ ♦ rs ♠tt♥ ♠t strt♦♥ ♥t♦♥ ts rsts r ♣rt ♦ t rt♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t ❬❱❪

Page 13: Model-based clustering for categorical and mixed data sets
Page 14: Model-based clustering for categorical and mixed data sets

♥ rt♦♥s ♥ ♥♦tt♦♥s

♥ rt♦♥s

♥r

♠♣ ♠①♠♠ ♣♦str♦r♠♣ ♠①♠♠ ♣♦str♦r st♠t♠ ♠①♠♠ ♦♦ st♠t ♠t strt♦♥ ♥t♦♥♣ ♣r♦t② strt♦♥ ♥t♦♥

♦rt♠s

♠ ①♣tt♦♥①♠③t♦♥ ♦rt♠♠ ♥r③ ①♣tt♦♥①♠③t♦♥ ♦rt♠

♠♠ r♦ ♥ ♦♥t r♦s♠ t♦st ①♣tt♦♥①♠③t♦♥ ♦rt♠

♥♦r♠t♦♥ rtr

♥♦r♠t♦♥ rtr♦♥ ②s♥ ♥♦r♠t♦♥ rtr♦♥ ♥trt ♦♠♣t♦♦

♥ ♥♦tt♦♥s

♥♦tt♦♥s ♥ ♥ t t ♦♦♥ rs

rs r ♥♦t t r ttrs t ♣r♠trs r ♥♦t t r ttrs

♠t♠♥s♦♥ ♦ts r ♥♦t ② ♦ s②♠♦s t ♥♠♥s♦♥ ♦ts r ♥♦t ② t♥ s②♠♦s

Page 15: Model-based clustering for categorical and mixed data sets

♥ rt♦♥s ♥ ♥♦tt♦♥s

❱rs ♥ ♦srt♦♥s

X i st ♦ t e r♥♦♠ rs rt t♦ ♥ ixi ♦sr s ♦ X i

x′i tr♥s♣♦s ♦ xi

x

i sst ♦ xi ♦♠♣♦s ♦ t c ♦♥t♥♦s rsx

i sst ♦ xi ♦♠♣♦s ♦ t d srt rsmj ♥♠r ♦ ♠♦ts ♦ r jZi r♥♦♠ r ♦ t ss ♠♠rs♣ ♦ t ♥ izi ♦sr s ♦ Zi

yi s♦♥ t♥t r rt t♦ ♥ i rqr① n s♠♣ ① = (x1, . . . ,xn)③ n s♠♣ ③ = (z1, . . . , zn)② n s♠♣ ② = (y1, . . . ,yn)

Pr♠trs

θ ♦ ♣r♠trs ♦ t ♠①trπ t♦r ♦ ♣r♦♣♦rt♦♥sαk ♣r♠trs rt t♦ ♦♠♣♦♥♥t kΓk ♠tr① ♦ t ♦rrt♦♥ rt t♦ ♦♠♣♦♥♥t kν ♥♠r ♦ ♣r♠trs

♠♣♦rt♥t ♥trs

c ♥♠r ♦ ♦♥t♥♦s rsd ♥♠r ♦ t♦r rse ♥♠r ♦ rs c+ d = eg ♥♠r ♦ sssn s③ ♦ t s♠♣nk s③ ♦ ss k ♦♠♣t ♦♥ t ③③② ♣rtt♦♥♥k s③ ♦ ss k ♦♠♣t ♦♥ t r ♣rtt♦♥

ss strt♦♥s

Dg(.) rt strt♦♥ ♦ s③ gG(.) ♠♠ strt♦♥

Nc(µ,Σ) crt ss♥ strt♦♥ t ♠♥ µ ♥ ♦r♥ ♠tr① Σ

Mg(.) ♠t♥♦♠ strt♦♥ ♦ s③ gP(.) P♦ss♦♥ strt♦♥

Page 16: Model-based clustering for categorical and mixed data sets

♥ rt♦♥s ♥ ♥♦tt♦♥s

ss t♦♦s

p(.; .) ♣r♦t② strt♦♥ ♥t♦♥P (.; .) ♠t strt♦♥ ♥t♦♥

φc(.;µ,Σ) ♣ ♦ Nc(µ,Σ)Φc(.;µ,Σ) ♦ Nc(µ,Σ)

Φ1(.) ♣ ♦ N1(0, 1)KL(f1, f2) r r♥ r♦♠ f1 t♦ f2 f2 rr♥

p(①;θ) ♦srt ♦♦L(θ;①) ♦srt ♦♦♦

p(①, ③;θ) ♦♠♣tt ♦♦L(θ;①, ③) ♦♠♣tt ♦♦♦

tik(θ) ♣r♦t② tt xi s r♥ ② ♦♠♣♦♥♥t k

Page 17: Model-based clustering for categorical and mixed data sets
Page 18: Model-based clustering for categorical and mixed data sets

♣tr

str ♥②ss stt ♦ t rt

♠♥ ♣r♣♦s ♦ ts ♣tr s t♦ r t trtr ♦t str ♥②ss ♦t tt ♦r ♠ s ♥♦tt♦ ①st ts ♣r♥♣② ♦s ♦♥ t ♠♦s ♣♣r♦s ♥ ♦rr t♦ ♥ t r♥t ♥♦t♦♥s♦♣ ♥ ts ♠♥sr♣trst② ♣rs♥t r♥t ♣♣r♦s ♦♠tr ♥♣r♦st t♦ str t t ♦♥② rt rq♥tst ♥ t ②s♥ ♣♣r♦s s t♦ ♥r t ♥t ♠①tr ♠♦s ♥② ♣rs♥t s♦♠rtr ♣r♦r♠♥ t ♠♦ st♦♥ ♥ ♣r♦st♦♥t①t♦ t♦② ①♠♣s strt t r♥t ♥♦t♦♥s ♥ t♦rt♠s tr♦ ts ♣tr ♥ ♦♥t♥♦s ss♥ t s t sst ♦♥

♦♦ st♦r② ② ♥ t♦ t♥s ♠st ♥ ♦t ♦ t

rrs ♦♥ ①♣r♥♦♥ t♥ ♦rt t

r ♦ t str♥ ♣♣r♦s

str♥ ♥

♦②s ♣rtt♦♥rs r ♦t♥ ♥ ♦♠♣① t sts tt ♥♦t ♥ts ♠♥sr♣t ② x = (x1, . . . ,xn) sr♥ n ♥s xi = (x1i , . . . , x

ei ) ② e

rs s ♦♠♣①t② s ♥r② ♥♦ ② t r ♥♠r ♦ ♥s♦r♠♥ t ♣rtt♦♥rs ♥r ♠ ♥♦r♠t♦♥s rtr♠♦r ts♦♠♣①t② ♥ ♥rs ② t sr♣t♦rs t♦ t ♥♠r ♦ rs ♦r t♦tr ♥tr ♦r ♥st♥ t♦r ♦r ♠① rs

str♥ s ♥r ♥sr t♦ ts ♣r♦♠ ♥rs♥② ♠rs tt ♦♠♣tr ♦♣♠♥t ♥ ts t♥q s♠♠r③s t t ② r♦♣♥

Page 19: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

t ♥s ♥t♦ g sss ♦r♥ t♦ ♦t ♦♦♥ ♣r♥♣s t ss ♦♠♦♥t② r♦♣♥ s♠r ♥s ♥t♦ t s♠ ss ♥ t ss s♣rt②t♦ ♥s rs♥ r♦♠ t♦ r♥t sss r str♦♥② r♥t ♥ t ①t ♥t♦♥ ♦ ss s s♣ t♦ t str♥ ♠t♦ st ② t♣rtt♦♥r t s ②s s ♦♥ ts t♦ ♣r♥♣s

♦r♥ t♦ ts ♣r♥♣s t str♥ ♠t♦s tr② t♦ tr♠♥ t t♥tt♦r z = (z1, . . . , zn) r t t♦r zi = (zi1, . . . , zig) ♥ts t ss ♠♠rs♣ ♦ t ♥ xi ② s♥ ♦♠♣t s♥t ♦♥ zik = 1 xi s t ♥t♦ ss k ♥ zik = 0 ♦trs ♦t tt sss t♦ ♥tr♣rt ♦r t s♣st ♦ t ♦♠♥ r t t ♦♠ r♦♠ ♥ str♥ ♠t♦ ♣r♦s ♥ ♥t s♠♠r② ♦ t t ♦♥② ts rst♥sss r ♠♥♥

ss ♠♠rs♣s ♦ t ♥s t♦ st♠t t t ♥♠r♦ sss s ♥r② ♥♥♦♥ s ♥ ♥t str♥ ♠t♦ ♣r♦s t♦♦st♦ ♣ t ♣rtt♦♥r ♦r t st♦♥ ♦ t ♥♠r ♦ sss str♥s tr ♠♥ ♦s t♦ st♠t t ♣rtt♦♥ t♦ ♣r♦ ♠♥♥ sss ♥t♦ st t♦♠t② t ♥♠r ♦ sss

♦r t r t sts n ♥ e r ♣rtt♦♥rs ♥ s♠t♥♦s② strt ♥s ♥ t rs ♦ ♣rtt♦♥s ♥ ts sr ♦♥ ♠♦♥t ♥s ♥ ♦♥ ♠♦♥ t rs s ♣♣r♦ s ♥♠ ♦str♥❬ ❪ t t s ♥♦t ♦♣ ♥ ts tss r ♦♥② st② t str♥♣r♦♠

♠t♦s ♣r♦r♠♥ t str ♥②ss r ♥t♦ t♦ r ♠st ♦♠tr ♠t♦s s ♦♥ s♦♠ st♥s t♥ ♥s ♥ t ♣r♦st ♠t♦s ♠♦③♥ t t ♥rt♦♥ ♥ ts st♦♥ ♦t ♣♣r♦sr t ♥ ♥r r♠♦r rtss t② r strt ♦♥ rt♦♥t♥♦s t st ♣rs♥t ♦ s♥ t ♦s t♦ s② s③ t rsts♦ s♣ stts ♦ t rt rt t♦ ♠♦r ♦♠♣① stt♦♥s t♦r ♥♠① t sts r ♥ tr ♥ t ♥tr♦t♦♥s ♦ Prt ♥ Prt

t t st ❬❪ s ♦♥ t ♣ ♠ss st st ♦♥t♥s t t♥ t♠ t♥ r♣t♦♥s ♥ t rt♦♥♦ t r♣t♦♥s ♦r t t ②sr ♥ ❨♦st♦♥ t♦♥ Pr❲②♦♠♥ ❯ s♣② ② r ♠ s t♦ ♣r♦ ♠♥♥ s♠♠r② ♦ t t st x = (x1, . . . ,xn) r ♥ xi ∈ R

2s ♥ ts ①♠♣ n = 272 ♥ e = 2

t t st t ♣rs♥tt♦♥

Page 20: Model-based clustering for categorical and mixed data sets

r ♦ t str♥ ♣♣r♦s

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

5060

7080

90

eruptions

wai

ting

r t t st

♦♠tr ♣♣r♦s

♥rts

♥ ♦♠tr ♣♣r♦s r♦♣ t st ♦ t str♥ ♠t♦ss ♦♥ t st♥ ♠sr♠♥t t♥ ♥s ♥ ♦rr t♦ ss♥ t ♠♦sts♠r ♥s ♥t♦ t s♠ ss ② ①rt♥ t ss ♦♠♦♥t② ♦♥♣t ♥ ♥trt ♦t ♦ t ♦♦♥ t♦ ♥s rs♥ r♦♠t s♠ ss t♦ ♠♦r s♠r t♥ t♦ ♥s ♥ r♥t ss♠♠rs♣ s ♠ ♥ ①♣rss ② t ♦♦♥ ♠t♠t rt♦♥♦r st♥ D(., .) ♥ ♦r (i0, i1, i2, i3) s tt zi0 = zi1 ♥ zi2 6= zi3 :

D(xi0 ,xi1) ≤ D(xi2 ,xi3).

Pr ♣r♦♠ ♥ s rt♦♥ s t♦ sts ② t qr♣ts ts ♦t ♥r② s t♦ ♠♣t② st s♦t♦♥ ♥♠r ♦ ♦♥str♥ts rtr♠♦r t s ♥♦t r③ t♦ ♣r♦r♠ ♥ ①st ♣♣r♦ ♦♠♣ts t ♦t rtr♦♥ ♦r t ♣♦ss ♣rtt♦♥s ♥ ♦r s♠♣♦ s③ n = 40 tt str ♥ g = 3 sss t ♥♠r ♦ t ♣♦ss ♣rtt♦♥ss r♦② q t♦ 2.1018 s ♦♠♣tr ♣r♦r♠♥ 109 ♣rtt♦♥s ♣r s♦♥♥s 64000 ②rs t♦ t t ♣♦ssts

♦ rtr♦♥ ♥ ♣rt t s s t♦ ♦♣t♠③ ♦ rtr♦♥ rt♥t ss ♦♠♦♥t② r♥t rtr ♦t♥ s ♦♥ rst s ♥s♦ ♣r♦♣♦s s t r♥♥♥ ①♠♣ s rtr r s② ♦♣t♠③ ② ♥♦rt♠ ♦♥ ♥ ①st ♣♣r♦ s ♥trt

trtr ♦ ts st♦♥ rst② ♣rs♥t t tr ♠♦st ♦♠♠♦♥ rtr♦ ♥trst ♥ t rs r ♦♥t♥♦s ♦♥② t t ♠♥s

Page 21: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

♦rt♠ s t ♠♦st ss ♦♠tr ♣♣r♦ t♦ str ♥s sr ② ♦♥t♥♦s rs ❬r❪ ♥② ts ♦rt♠ s strt ♦♥ tt t st

t s ♥tr♦ t ♠tr① ♦ t ♦ s♠♣ ♦r♥ ♥♦t ② T

♥ ♥ s

T =1

n

n∑

i=1

(xi − x)(xi − x)′,

r x = 1n

∑ni=1 xi s t ♠♥ t♦r ♦ t ♦ s♠♣ s ♠tr①

♥ rtt♥ s s♠ ♦ t♦ ♠trs

T = W +B,

r t ♥trss ♦r♥ ♠tr① W ♥ r t ♥trss ♦r♥ ♠tr① B r ♥ ②

W =1

n

g∑

k=1

n∑

i=1

(xi − xk)(xi − xk)′ ♥ B =

1

n

g∑

k=1

♥k(xk − x)(xk − x)′,

xk = 1

♥k

∑ni=1 zikxi s t ♠♥ t♦r ♥ ♥k =

∑ni=1 zik t s③ ♦ ss

k s ♠trs rtr③ ♦t ♦♥strt♦♥s ♣r♥♣s ♦ t sss♥ sss r ♦♠♦♥♦s t♥ t st♥s t♥ t ♥s ss♥ t♦ ss ♥ ts ♥tr r s♠ s♦ W s s♠ sss r s♣rt t♥ t ♥trs ♦ t sss r ♠t② t♥② s♦ B s rs t ♣rtt♦♥r ♥ str t t t st ② ♦♣t♠③♥ r♥t rtr rt t♦ ts ♠trs ♠♦♥ t♠ t ♠♦st ssr t ♦♦♥ min tr(W ) min det(W ) ♦r max tr(BW−1) ❲rr t♦ t ♦♦ str ♥②ss ② rtt ♥ s ♥ t ❬❪ ♦r ♠♦r ts

t t st ♣t♠③ rtr ♦r ♦♥t♥♦s rs

♠♥s ♦rt♠

♥ ♦② ♣r♦♣♦s t ♠♥s ♦rt♠ r♦♥ ♥ t t♦ ♣s t ❬♦❪ ss♦t t♦ st♥ D(., .) ts ♦rt♠ ♠s t♠♥♠③♥ t ♦♦♥ ♥rt

I(z,θ;x) =n∑

i=1

g∑

k=1

zikD2(xi,µk),

r θ = (µ1, . . . ,µk) ♥ r µk s t ♥tr ♦ t ss k trt♥ r♦♠♥ ♥t ♦ t ss ♥trs t ♠♥s ♦rt♠ tr♥ts t♥ t♦st♣s t ss♥♠♥t ♦ ♥ t♦ t ss ♠♥♠③♥ t st♥ t♥♠ ♥ t ss ♥tr ♥ t ♦♠♣tt♦♥ ♦ t ss ♥trs

Page 22: Model-based clustering for categorical and mixed data sets

r ♦ t str♥ ♣♣r♦s

trt♥ r♦♠ ♥ ♥t θ[0] trt♦♥ [r] s rtt♥ s ♦♦s ss ♠♠rs♣ z

[r] = argminz

I(z,θ[r];x)

z[r]ik =

1 k = argmink′

D2(xi,µ[r]k′ )

0 ♦trs

♥tr♦ st♠t♦♥ θ[r+1] = argminθ

I(z[r],θ;x)

µ[r+1]k =

1

♥[r]k

n∑

i=1

z[r]ik xi,

r ♥[r]k =

∑ni=1 z

[r]ik

♦rt♠ ♠♥s ♦rt♠

s ts ♦rt♠ ♦♥rs t♦ ♦ ♦♣t♠♠ ♦ I(z,θ;x) t s ♠♥t♦r②t♦ ♣r♦r♠ r♥t ♥t③t♦♥s ♥ t♦ ♣ t ♦♣ (z,θ) ♠♥♠③♥ t♦t ♥rt

♠r ♠♥s ♦rt♠ ♥ ♦♣t♠③ rtr♦♥ t ♠♥s ♦rt♠ strs ♦♥t♥♦s t ② s♥ t ♥ st♥ t♥ t ♦♣t♠③st rtr♦♥ min tr(W )

①t♥s♦♥s ♦ t ♠♥s ♦rt♠ ♦♠ ♣♣r♦s tt♠♣t t♦ rt rs ♦ t ♠♥s ♦rt♠ ♦r ♥st♥ t ♠♥s ♦rt♠❬❱❪ ①t♥s t ss ♦♥ ② r♥♦♠③ s♥ t♥q ♠♣r♦♥ ts♣ ♥ t r② ♦ t ♠♥s

♦ ♠♥② sss st♦♥ ♦ t ♥♠r ♦ sss ♥ ♥♦t rt②♣r♦r♠ ② t ♥rt rtr♦♥ ♥ ♥ s♥ ts ttr s rs♥ tt ♥♠r ♦ sss g ♦r t ♦t rtr♦♥ rs ♣t ♥g ♥rss ♥ ♥ ts ♣t s r t sss r ♥♦ ♠♦r♦♠♦♥♦s t ss ♦r♣♣♥ ♥rss rst rtr♦♥ ♦♥ssts ♥st♥ t rst ♥♠r ♦ sss ♦ ts ♣t t t s r tt ts ♥♦ rtr♦♥ s ♥♦t r② r♦r♦s ♥ ♣rt t rtr♦♥ ♥ ♥♣ ♥s♦♠ ♣ts r ♦sr tr rtr r s ♦r ♥st♥ ❬r❪t t② r s ♦♥ rst ♣♣r♦

Page 23: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

❲ s t ♠♥s ♦rt♠ t♦ str t t t st ♦r♥t♦ r r♥ t ♦t♦♥ ♦ t ♥rt ♦r r♥t ♥♠rs ♦sss r♦♠ ♦♥ t♦ t ♥ st t♦ sss ♣rtt♦♥ ♥t ss ♥trs r s♣② ② r t t sts s♦ s♠♠r③ ② t♦ ♣r♦s ♦ r♣t♦♥s t r♣t♦♥s t s♦rtt♥ t♠ ♥ rt♦♥ ♥tr t (2.09, 54.75) r s♣② t rs ♥ t r♣t♦♥s t rr t♥ t♠ ♥ rt♦♥ ♥tr t(4.30, 80.28) r s♣② t r tr♥s

t t st ♠♥s ♦rt♠ ♣♣r♦

1 2 3 4 5 6 7 8number of classes

iner

tia0

1000

020

000

3000

040

000

5000

0

♥rt

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

5060

7080

90

eruptions

wai

ting

ttr ♣♦t ♥ ss ♥trs

r t♣ts ♦ t t str ♥②ss ♣r♦r♠ ② ♠♥s ♦rt♠ sttr ♣♦t ♥ts t ♣rtt♦♥ ② t ♦♦rs ♥ t t♥ s②♠♦s t ss ♥trs r r♣rs♥t ② t ♦ s②♠♦s ♥ t ♦♦r ♦ tr ss

♠ts ♦ t ♦♠tr ♣♣r♦s

r♥t t♦rs ♣♦♥t ♦t t rs ♥r♥t t♦ t ♦♠tr ♣♣r♦ss ♦r ♥st♥ t ♦♦ t ♥②ss ♣tr ② ♦rt ❬♦❪ ♦st tr ♠♥ rs r ♣rs♥t r

♦ st♦♥ ♣r♦r♠ ② rst ♣♣r♦s ♥ t ♦♠tr ♣♣r♦s r ♣♦ss ♥sr t♦ t str♥ ♥ ♠♥② t♦rt♣r♦♠s ♠② rs ❬♦❪ ② ♥r② st t ♥♠r ♦ sss ② s♥ rst ♣♣r♦s t s♦♣ ♦ t rtr♦♥ s rtr♠♦r ♦t♦s ♦ t ♠tr ♥ t rtr♦♥ s r ♠♣♦rt♥t s♣ts ♦ ts ♠t♦s s♥ t② ♥♦ ♠♥② ♥ ss♠♣t♦♥s r ♥r② ♥♦r ② t♣rtt♦♥rs ♦r tr ♠♣t s r s t② ♥♦ r♥t ♣rtt♦♥s

Page 24: Model-based clustering for categorical and mixed data sets

r ♦ t str♥ ♣♣r♦s

①t♥s♦♥ ♦ t ♦♥s♦♥s t♦ t ♦ ♣♦♣t♦♥ t ♦♥s♦♥s ♦ str ♥②ss s ♦♥ s♠♣ t♦ ①t♥ t♦ t ♦ ♣♦♣t♦♥t s ♠♥t♦r② t♦ ♥rst♥ s♦ t♦ ♠♦③ t t ♥rt♦♥ ①t♥s♦♥♦ t ♦♥s♦♥s ♦t♥ ② ♦♠tr ♠t♦ r ♥♦t ♦ ♥ s st ♣r♦st r♠♦r s s♦ ♠♥t♦r②

♥ t ♠ss♥ s ♦♠tr ♣♣r♦s ♥♥♦t rt② ♠♥t sts t ♠ss♥ s ♥ t② t♦ ♣r♦r♠ ♥ rtrr② ♠♣tt♦♥ ♦r t② t♦ ♥♦r ♥s t ♠ss♥ s t ♣r♦st♣♣r♦s r t♦ r♦r♦s② ♠♥ s t

♥s t♥ ♦♠tr ♥ ♥rt ♣♣r♦s

♥② ♦♠tr ♣♣r♦s ♥ ♥tr♣rt s ♣r♦st ♦♥s r♥tr ♣r♦st ♥ ss♠♣t♦♥s s♦♠ ①♠♣s r ♥ ♥ ts tss ♥♦rr t♦ ♣r♦st t♦♦s ♥ t♦ r t ss♠♣t♦♥s ♠ ② t str♥♠t♦s ♦♣ ts tss ♥ ♣r♦st r♠♦r

♥rt ♣♣r♦s

♥rts

♥ ♠①tr ♠♦s r ♥tr t♦♦s t♦ str t t ② ♣♣r♦♥ tr strt♦♥s s tr ♣r♦st r♠♦r ①♣♥s t t ♥rt♦♥ ♥ ts ♦♥t①t t ♥♦t♦♥ ♦ ss ♦♠♦♥t② s ♥ ② t ♦♦♥ t ♥s ♦ t s♠ ss rs r♦♠ t s♠ ♣r♦t② strt♦♥ ts strt♦♥ s ♥r② ss♠ t♦ ♥♠♦ ts ss♠♣t♦♥ ♥ r① ♦r ♥st♥ t ♦♠♣♦♥♥t strt♦♥ ♥ ts ♠①tr ♦ ♣r♠trstrt♦♥s t♦ ♥rs t ♠♦ ①t② ❬+❪

t♥t r ♥ ss ♠♠rs♣ ss ♠♠rs♣ ♦ t ♥ i s qtt r♥♦♠ r ♥ g ♠♦ts ♥ ♥♦t ② Zi =(Zi1, . . . , Zig) ② s♥ s♥t ♦♥ s t ss ♠♠rs♣ ♦♦s ♠t♥♦♠ strt♦♥

Zi ∼ Mg(π1, . . . , πg),

r πk ♥♦ts t ♣r♦♣♦rt♦♥ ♦ ss k s♦ ♥tr♣rt s t ♣r♦t② ♣r♦r tt ♥ ♥ rss r♦♠ ss k ss ♣r♦♣♦rt♦♥ πk rs♣ts♦t ♦♦♥ ♦♥str♥ts 0 < πk ≤ 1 ♥

∑gk=1 πk = 1 ♦t tt t str♥

♥ s t♦ st♠t t ♦ t r③t♦♥ zi ♦ t t♥t r Zi

♦♥t♦♥② ♦♥ t ♦sr t xi

sr rs ss k s rtr③ ② t strt♦♥ ♦ t ertr♥♦♠ r X i = (X1

i , . . . , Xei ) ♥ ♦♥ t s♣ X ♦♥t♦♥② ♦♥ t

r③t♦♥ zi ♦ t r♥♦♠ r Zi s strt♦♥ s ♥♦t ② pk(xi)r k s s tt zik = 1 ♥

X i|Zi = zi ∼ pk:zik=1(xi).

Page 25: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

strt♦♥ ♦ ♦t ♦sr ♥ t♥t rs ② s♥ ts ♣r♦t②♦♠♣♦st♦♥ P (X i,Zi) = P (Zi)P (X i|Zi) t ♣r♦t② strt♦♥ ♥t♦♥♣ ♦ (xi, zi) ♥♦t ② t ♥r ♥♦tt♦♥ p(.) s ♥ s ♦♦s

p(xi, zi) =

g∏

k=1

(πkpk(xi))zik .

♥ ts ♠♦ s s t♦ str t s zi r ♦♥sr s ♠ss♥ ss ♦t♥ ♦t ♥t♦♥s ♦ t ♥t ♠①tr ♠♦ ♥ ts ♥rt♠♦ ② s♠♠♥ t ♣r♦s qt♦♥ ♦r t ♣♦ss s ♦ Zi

♥t♦♥ ♥t ♠①tr ♠♦ ♥t ♠①tr ♠♦ t g ♦♠♣♦♥♥ts ♥s t ♠r♥ strt♦♥ ♦ t r♥♦♠ r X i ts ♣ s rtt♥s

p(xi) =

g∑

k=1

πkpk(xi).

♥rt ♠♦ s♠♣♥ r♦♠ t ♠①tr ♠♦ ♥ ② s♣r♦r♠ ② t ♦♦♥ ♥rt ♠♦ ♥t♦ t♦ st♣s

t♣ t ss ♠♠rs♣ s♠♣♥ Zi ∼ Mg(π1, . . . , πg t♣ t ♦♥t♦♥ t s♠♣♥ X i|Zi = zi ∼ pk:zik=1(xi)

sst♦♥ r

③③② ♥ r ♣rtt♦♥ ❲♥ t t strt♦♥ p(xi) s ♥♦♥ t ♥t♦♥ ♦ Zi|X i = xi s strt♦rr

Zi|X i = xi ∼ Mg(ti1, . . . , tig),

r tik s t ♦♥t♦♥ ♣r♦t② tt xi s r♥ r♦♠ ♦♠♣♦♥♥t k s♥ ②

tik =P (Zik = 1,X i = xi)

P (X i = xi)=πkpk(xi)

p(xi).

❱t♦r ti = (ti1, . . . , tig) s♦ ♥s ③③② ♣rtt♦♥ ♥ s t♦ ♦♠♣tt rs ss♦t t♦ t r ♣rtt♦♥ zi

rr♦r rs ♥ sst♦♥ r r♦♠ ts ③③② ♣rtt♦♥ ♥ ♥ tsst♦♥ rr♦r e(.) ss♦t t♦ (zi, ti) ②

e(zi, ti) = 1−g∑

k=1

(tik)zik .

♠①♠♠ ♣♦str♦r r ♠♣ ♠♥♠③s t sst♦♥ rr♦r ② ss♥♥♥ ♥ ♥t♦ t ss ♥ t rst ♣r♦t② s t ♥s tsst♦♥ r r(.) s ♦♦s

∀xi ∈ X , r(xi) = k ∀k′ tik ≥ tik′ .

t♦♥ ♦ t rs ♦ t sst♦♥ rr♦r s rt ♥t ♦ t ♣r♦st ♠t♦s s♥ t ♦♠tr ♦♥s ♥♥♦t q♥t② t rr♦r rs ss♦tt♦ tr sst♦♥ r

Page 26: Model-based clustering for categorical and mixed data sets

♥rts ♦♥ ♥t ♠①tr ♠♦s

♥rts ♦♥ ♥t ♠①tr ♠♦s

♥ s ♠♦s ss♠ tt t ♦sr ♥s r ♥♣♥♥t②r♥ r♦♠ t s♠ strt♦♥ ❲ ♥♦ q② sr t s♠♣r♠tr♠①tr ♠♦s ♠ ss♠♣t♦♥s ♦♥ t ♦♠♣♦♥♥t strt♦♥s ♥ sr t ♣r♠tr ♠①tr ♠♦s ss♠ tt t ♦♠♣♦♥♥tstrt♦♥s r ♣r♠tr ♦♥s ♦t tt t ♠♦ sr♣t♦♥ ♣rs♥t rs s ♦♥ ♥t ♠①tr ♠♦s ② ♥ ♥ P ❬P❪ ♥ tstss ♦s ♦♥ t ♣r♠tr ♠①tr ♠♦s s♥ t② ♣r♠t ♥ sr♥tr♣rtt♦♥ ♦ t sss

♠♣r♠tr ♠①tr ♠♦s

♦♥str♥ts ♦♥ t ♦♠♣♦♥♥t strt♦♥s s♠♣r♠tr ♣♣r♦s ♦ ♥♦t ss♠ tt t ♦♠♣♦♥♥ts ♦♦ ♣r♠tr strt♦♥s ♦r ♦r rs♦♥s ♦ ♥tt② s♦♠ ♦♥str♥ts t♦ ♠♣♦s ♦r t ♦♠♣♦♥♥ts ♦r ♥st♥ strt♦♥s t♦ ♦♥ t♦ t ♠② ♦ ♥♠♦strt♦♥s ♦r s②♠♠tr strt♦♥s ❬❲❪

♥r♥ st♠t♦♥ ♦ t ♦♠♣♦♥♥t strt♦♥s ♥ ♣r♦r♠ ②♦rt♠s ♥s♣r r♦♠ t ♠ ♦rt♠ ❬❪ ss r♥ ♣♣r♦s❬❪ ♣ ♠①t♦♦s ❬❪ ♦s s t♦ str t t ② s♥ s♠♣r♠tr ♠①tr ♠♦

♦♥♥tt② rs s♠♣r♠tr ♠①tr ♠♦s r r② ①s♦ t② ♥ s② t t t strt♦♥ ♦r ts ①t② ♥♦s ♥♠♣♦rt♥t rs ♦ ♥♦♥♥tt② ♥ r r♥ ♦ t st♠t ♠♦rtr♠♦r t ss ♥tr♣rtt♦♥ ♥ t s♥ t ♦♠♣♦♥♥ts ♥ ♥♦t s♠♠r③ ② ♣r♠trs s ♥ t ♣r♠tr ♠①tr ♠♦s ♦♥ ts tss ♦♥② st② t ♣r♠tr ♠①tr ♠♦s ♦r t ♥r♣r♦♣rts r ♥♦ ♦♣

♣r♠tr ♠①tr ♠♦s

♥rts

♥ s ♠♦s ♠ t s♣♣♠♥tr② ss♠♣t♦♥ tt ♦♠♣♦♥♥t ♦♦s ♣r♠tr strt♦♥ s♦ pk(xi) = p(xi;αk) r αk r♦♣s t♣r♠trs ♦ ♦♠♣♦♥♥t k ♥s r s♦ r♥ ② ♣r♠tr strt♦♥ p(xi) = p(xi;θ) r θ = (π,α) ♥♦ts t ♦ ♣r♠tr rπ = (π1, . . . , πg) s t t♦r ♦ t ss ♣r♦♣♦rt♦♥s ♥ r α = (α1, . . . ,αg)r♦♣s t ♣r♠trs ♦ t ♦♠♣♦♥♥ts

♥t♦♥ ♥t ♣r♠tr ♠①tr ♠♦ ♣ ♦ t ♥t ♣r♠tr♠①tr ♠♦s t g ♦♠♣♦♥♥ts s ♥ ②

p(xi;θ) =

g∑

k=1

πkp(xi;αk).

Page 27: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

♥tr♣rtt♦♥ t ♣r♠trs s ♠♦s r ♠♦r ♠♥♥ t♥ ts♠♣r♠tr ♣♣r♦s s♥ ss ♥ s♠♠r③ ② ts ♣r♦♣♦rt♦♥ πk♥ t ♣r♠trs ♦ ts strt♦♥αk ♣r♦ts ♦ t ss ♠♠rs♣stik r s♦ ♣r♠tr③ ② θ s♦ t② r ♥♦ ♥♦t ② tik(θ) t

tik(θ) =πkp(xi;αk)

∑gk′=1 πk′p(xi;αk′)

.

♦♠♣♦♥♥ts tr ♦ t♥ r♥ ♥ ♥♠r s ♣♣r♦ s♥♦t r② rstrt s♥ ♠①tr ♦ ♣r♠tr strt♦♥s ♥ ♣♣r♦ ♥②strt♦♥ t ♥② ♣rs♦♥ t strt♦♥s ♦♠♣♦♥♥t strt♦♥s ♥♣♣r♦ strt♦♥ t s♠ s♣♣♦rt st ② ♥rs♥ t ♥♠r ♦♦♠♣♦♥♥ts ♥ t s♠♣ s③ s t ♦♦♥ ①♠♣ s ♠①tr ♦st♥r strt♦♥s ♥ ♠♦③ ② ♦♠♣① strt♦♥s ♦r t♠①tr s ♠♦r ♠♥♥ ♥ t ♥♠r ♦ sss st②s s♠ rtr♠♦rs t ♠①tr ♠♦ s st♠t ♦♥ ♥t s♠♣ t ♣rtt♦♥r srs t♠♦ ♣r♦r♠♥ t st tr ♦ t♥ ts s t t tr ♠♦ ♥ts r♥ s ② t tt♦♥s ♦ t s♠♣♥ r♦r t s ♠♣♦rt♥ttt t ♦♠♣♦♥♥ts ♦♦ st♥r strt♦♥s r ♣t t♦ t t

①♠♣ ♥st② st♠t♦♥ ♥ Pr③♥♦s♥tt st♠t♦r t x t♦ t s♠♣ ♦ s③ n r ♥ xi ∈ R s ♥♣♥♥t② r♥ ② tstrt♦♥ rtr③ ② ts ♣ f(x) Pr③♥♦s♥tt st♠t♦r ❬Pr❪s ♥ s

p(y;θ) =n∑

i=1

1

nhK

(

y − xih

)

.

♦t tt ♥s ♥t ♠①tr ♠♦ t n ♦♠♣♦♥♥ts r ♣r♦♣♦rt♦♥ s q t♦ 1/(nh) ♥ r t strt♦♥ ♦ ♦♠♣♦♥♥t i s ♣r♠tr③② ♦♥ xi ♦r i = 1, . . . , n ❯♥r s♦♠ rrt② ♦♥t♦♥s ♦♥ t ♥t♦♥ K(.)♥ ♥r s♦♠ rt♦♥s t♥ t s♠♣ s③ n ♥ t ♥t h t ♣p(y;θ) ♦♥rs t♦ t tr ♣ f(x) s ♦r ♥st♥ ❬❩❪ ♦r ♠♦r tsr strts t Pr③♥♦s♥tt st♠t♦r ♣♣r♦♥ ♣ ② s♥t ♥♦r♠ r♥ ♦r t ss♥ ♦♥

K♥♦r♠(y) =

1/2 |y| < 10 ♦trs

♥ Kss♥(y) =1√2πe−

12y2 .

①tr ♥ ♥ ♦ rs ♣r♠tr ♠①tr ♠♦s ♥ str♥s ② ♣♣r♦♥ t strt♦♥ ♦ t rs ♥ tr ♥t s♣♦s② t strt♦♥s ♦ t ♦♠♣♦♥♥ts t♦ rs♣t t ♥tr ♦ trs s t ♠①tr ♠♦s r s t♦ ♥②③ r♥t ♥s ♦ t sts♦r ♥st♥ t ♠①tr ♦ P♦ss♦♥ strt♦♥s ❬❪ ♥ str ♥tr t t ♠①trs ♦ t♥t strt♦♥s ❬P❪ ♥ str t ♦♥t♥♦s ♦♥st t ♠①tr ♠♦s r s♦ s t♦ str ♥t♦rs ❬ ❲❪ r♥t ❬❪ ♦r ♥t♦♥ t ❬P❪ s ♦ t♦r t s ♦♣ ♥Prt Prt s ♦s ♦♥ t str♥ ♦ ♠① t ♦r t ♠♦st♦♠♠♦♥ ♠①tr ♠♦ s t ss♥ ♦♥ tt t ♥♦

Page 28: Model-based clustering for categorical and mixed data sets

♥rts ♦♥ ♥t ♠①tr ♠♦s

0 5 10 15

0.00

0.05

0.10

0.15

0.20

0.25

0.30

n = 3

0 5 10 15

0.00

0.05

0.10

0.15

0.20

0.25

0.30

n = 25

0 5 10 15

0.00

0.05

0.10

0.15

0.20

0.25

0.30

n = 100

0 5 10 15

0.00

0.05

0.10

0.15

0.20

0.25

0.30

n = 1000

r ♣ ♦ t tr strt♦♥ ♦tt r ♥ ts st♠ts♦t♥ ② t ♥♦r♠ r♥ t♥ r ♥ ② t ss♥ r♥ ♦r r r t ♥t s h = lnn

ss♥ ♠①tr ♠♦

♥ ss♥ ♠①tr ♠♦ s ♥tr♦ s♠t♥♦s② t♦ t♣r♠tr ♠①tr ♠♦ ♥ ♦rr t♦ str t Prs♦♥s r t st ❬P❪t s ♣♦r t♦♦ t♦ str ♦♥t♥♦s t ♦s sss s t♦ t♦ ♠♥rs♦♥s ♥ t ♦♥ ♥ ts ♣t ♥t♦♥ ♦ ss s ♥ ♦r♥ tt ♥tr ♥t♦♥ ♦ ss ♥ t ♦tr ♥ ts ♦♠♣tt♦♥ trtt②♣r♠ts ♥ s② ♥r♥ ss♥ ♠①tr ♠♦ ss♠s tt r♥♦♠r X i|Zik = 1 s ♥ ert ss♥ r ♦s t ♠♥ t♦r s♥♦t ② µk ♥ ♦s t ♦r♥ ♠tr① s ♥♦t ② Σk s♦

X i|Zik = 1 ∼ Ne(µk,Σk).

s ♦t♥ t ♦♦♥ ♥t♦♥ ♦ t ss♥ ♠①tr ♠♦

♥t♦♥ ss♥ ♠①tr ♠♦ t xi ∈ Re t ♦♥t♥♦s r

rs♥ r♦♠ ss♥ ♠①tr ♠♦ t g ♦♠♣♦♥♥ts ts ♣ s rtt♥ s

Page 29: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

♦♦s

p(xi;θ) =

g∑

k=1

πkp(xi;αk) t p(xi;αk) = φe(xi;µk,Σk),

r φe(xi;µk,Σk) =1

(2π)e/2|Σk|1/2exp

(

−12(xi − µk)

′Σ

−1k (xi − µk)

)

♥ rαk =

(µk,Σk)

ss ♥tr♣rtt♦♥ ss♥ ♠①tr ♠♦ ♣r♦s s♠♠r② ♦ ss tr♦♦t ts ♥tr ♣♦st♦♥ µk ♥ ts s♣rs♦♥ ♠tr① Σk rt♥ ts♣♥♥s t♥ t ♣rs ♦ rs

Prs♠♦♥♦s ♠♦s ❲♥ t s♠♣s r s♠ t ♥♦r♠t♦♥ ♦t t♥trss ♣♥♥s t♥ rs s ♥♦t ♣rs♥t ♥ t t ♥ ♥rt sr♥ tr ♦ ♠② ttr ♦♥str♥ts ♦♥ t ♣r♠tr s♣ r rst♥ ♠♦s r ♣rs♠♦♥♦s ♠♦s ♦r ♥st♥ s ♦♥t s♣tr ♦♠♣♦st♦♥ ♦ Σk ♣r♦♣♦s ♥ ❬❪ ♦rt♥ ♣rs♠♦♥♦s ♠♦sr t ❬❪ s ts ♠♦s r s♥st t♦ t ♥t ♦ ♠sr♠♥t ♦ trs ♥ ♠② ♦ ss♥ ♠①tr ♠♦ ♥♠ rt s ♣r♦♣♦s ② r♥ ♥ ♦r♠ ❬❪ ♥② ♥♦t tt t s♣tr ♦♠♣♦st♦♥♦ Σk ♥ s t♦ str ♠♥s♦♥ t ❬❪

♦trs ♥② s♦trs ♣r♦r♠♥ t st♠t♦♥ ♦ t ss♥ ♠①tr♠♦s r r ♠♣t ♥ t s♦♥ ♦ ts ♠♦s s s♠♦♥ t♠ ♦♥ ♥ t t tr ♦♦rs st ❬❪ ①♠♦ ❬+❪♥ ①t♦♦ ❬❨❪

s ♦t rs ♦ t t st r ♦♥t♥♦s st♠t ♦♠♣♦♥♥t ss♥ ♠①tr ♠♦ st♦r♠s ♦ ♦t rs r s♣② ♥ r ♥ t st♠t ♠r♥ ♣ ♦ ♦t ♦♠♣♦♥♥ts r s♣r♠♣♦s ♠♦ s♠♠r③s t t st s ♦♦s

♠♦rt② ss π1 = 0.64 s t ss ♦ t str♦♥ r♣t♦♥ss♥ µk = (4.29, 80.00)

♠♥♦rt② ss π2 = 0.36 s t ss ♦ t r♣t♦♥ss♥ µk = (2.04, 54.51)

ss ♦ t str♦♥ r♣t♦♥s s ♠♦r s♣rs t♥ t ss ♦ t r♣t♦♥s s♥ t r♥ ♦ ♦t rs r rr rs♣t②(0.15, 40.90) ♥ (0.10, 57.68) ♥ t rs r ♣♦st② ♦rrt ♥ ♦t sss tr ♣♥♥② str♥t s rr ♥ t ♠♥♦rt②ss t♥ ♥ t ♠♦rt② ♦♥ ♥ t ♦♥t ♦ ♦rrt♦♥ s qt♦ 0.50 ♥ t ss ♦ t r♣t♦♥s t s q t♦ 0.23 ♥ tss ♦ t str♦♥ r♣t♦♥s

t t st ss♥ ♠①tr ♠♦ str♥

Page 30: Model-based clustering for categorical and mixed data sets

♥rts ♦♥ ♥t ♠①tr ♠♦s

Den

sity

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

0.0

0.2

0.4

0.6

0.8

r♣t♦♥s

Den

sity

40 50 60 70 80 90

0.00

0.01

0.02

0.03

0.04

0.05

❲t♥

r st♦r♠s ♥ ♠r♥ ♥sts ♦ t ♦♠♣♦♥♥t ♠①tr ♠♦♦r t t st

❲ r♠r tt t s♠♠r② ♣r♦ ② t ss♥ ♠①tr ♠♦ s♠♦r ♣rs t♥ t s♠r② ♦t♥ ② t ♠♥s ♦rt♠ ♥ t ♠♥s ♦rt♠ ♦s ♥♦t ♦♥sr t ss ♣r♦♣♦rt♦♥s s♦ t♠♣t② ss♠s tt ♦t ♥s ♦ r♣t♦♥s r q♣r♦ rtr♠♦r t ss♥ ♠①tr ♠♦ ♣r♦s ♥ ♥②ss ♦ t ♥trss♣♥♥s s s♣② ② r rs t sttr ♣♦t ♦ t♣rtt♦♥ ♥ t ♣ss ♦ q♣r♦t②

t t st ♦♠♣rs♦♥ t♥ ♦t str♥ rsts

①tr ♠♦ t ♦♥t♦♥ ♥♣♥♥ ss♠♣

t♦♥

♥ ♦♥t♦♥ ♥♣♥♥ ♠♦ ♠ s♦ ♥♦♥ s ♥ ②s♦r t♥t ss ♠♦ s ♠①tr ♠♦ ss♠♥ t ♦♥t♦♥ ♥♣♥♥t♥ rs ♦ t ♦♥t♦♥ ♣r♦t② ♦ t ert r♥♦♠ rX i = (X1

i , . . . , Xei ) s rtt♥ s

P (X i|Zik = 1) =d∏

j=1

P (Xji |Zik = 1).

♦s② ts ss♠♣t♦♥ s r t♥ t ♦ ♥♣♥♥ ss♠♣t♦♥♥ t ♣♥♥② t♥ t rs s ♠♦③ ② t strtr ♥ sss♦ t strt♦♥

♥t♦♥ ♠ ♠♠♦ s ♠①tr ♠♦ ss♠♥ t ♦♥t♦♥♥♣♥♥ t♥ t rs s t ♣ ♦ t ♥ xi rs♥ r♦♠

Page 31: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

5060

7080

90

eruptions

wai

ting

r t♣ts ♦ t t str ♥②ss ♣r♦r♠ ② ♦♠♣♦♥♥tss♥ ♠①tr ♠♦ sttr ♣♦t ♥ts t ♣rtt♦♥ ② t ♦♦rs ♥t t♥ s②♠♦s t ♥trss ♣♥♥s r ♣t ② t ♣ss ♦q♣r♦t② ♦ t ss♥ ♦♠♣♦♥♥ts t ♥s ♦♥♥ t♦ t ss♦ t str♦♥ r♣t♦♥s r s♣② t r tr♥s ♥ t♦s ♦♥♥ t♦ tss ♦ t r♣t♦♥s r s♣② t rs

t ♠ ♠♦ s rtt♥ s

p(xi;θ) =

g∑

k=1

πk

d∏

j=1

p(xji ;αkj),

r αkj ♥♦ts t ♠r♥ ♣r♠trs rt t♦ r j ♦r ♦♠♣♦♥♥t k

①♠♣ ♦rrt t r♥ ② ♠ r s♣②s s♠♣ r♥② t ♠ ♠♦ t ♦r rt ss♥ ♦♠♣♦♥♥ts ♥ ts ①♠♣ t sstrt♦rr tt ♦t rs r ♥♦t ♥♣♥♥t

♥♥ rsts ♥ ♣rt ♠ ♠♦ ♦t♥s ♦♦ rsts ♥ ♣rts♥ t rqrs ♣r♠trs s sss ② ♥ ♥ ❨ ❬❨❪s t ♥ r③ ♦♦ tr ♦ t♥ t s ♥ t r♥ ts s♣rst②s rt ♥t ♦r t s♠ t sts s♥ t ♥♦r♠t♦♥ ♦ t ♥trss♣♥♥② s ♥r② ♥♦t ♣rs♥t sss ♦ t ♠ ♠♦ s s♦ ①♣♥② ts ♠♥♥ s♣t ♥ ♥ t ♠r♥ strt♦♥s ♦ t ♦♠♣♦♥♥tsr ss ♦r ♥st♥ ♥ t② ♦♥ t♦ t ①♣♦♥♥t ♠② ss♥ s♠♠r③ ② t ♣r♠trs ♦ ts ♠r♥s

s ♦ t ♥trss ♦rrt t ❲♥ t ♦♥t♦♥ ♥♣♥♥ss♠♣t♦♥ s ♦t t ♠ ♠♦ srs r♦♠ sr ss ♥ s s t ss ♥♠r s ♥♦♥ t♥ t ♣rtt♦♥ ♦♠s s t ss♥♠r s ♥♥♦♥ t♥ t ♠ ♠♦ ♦rst♠ts t t♦ ttr t t t s♦r ♥st♥ t ♣♣t♦♥ ♣rs♥t ♥ ❬❱❪

Page 32: Model-based clustering for categorical and mixed data sets

♥rts ♦♥ ♥t ♠①tr ♠♦s

0 2 4 6 8 10

02

46

8

X1

X2

Zi1 = 1Zi2 = 1Zi3 = 1Zi4 = 1

r rt s♠♣ rs♥ r♦♠ t ♠ ♠♦ t ♦ ♣♥♥②t♥ rs p(xi;θ) =

∑4k=1

14N2(2k, I)

①♠♣ s ♣rtt♦♥ t t ♦♠♣♦♥♥ts ♦♠♦sst ss♥♠①tr ♠♦ ♦s t ♣ ♦ xi ∈ R

2 s ♥♦t ② f(xi) = 13φ2(xi;µ1,Σ) +

23φ2(xi;µ2,Σ) r Σ =

(

1 ρρ 1

)

♥ ρ 6= 0 ♦♣t♠ sst♦♥ rs

♠♥♠③♥ t ②s rr♦r s

r②s(xi) : zik =

1 (xi − µk)′Σ−1(xi − µk) < (xi − µℓ)

′Σ−1(xi − µℓ) t ℓ = 2− k0 ♦trs

t t ♠ ♠♦ ♥ t s♠ ♦♥♠♥s♦♥ ♠r♥ strt♦♥s t♥ t♠♦ ♥ ② f(xi) ♣ ♦ ts ♠ ♠♦ s rtt♥ s

p(xi;θ) =1

3φ2(xi;µ1,Γ) +

2

3φ2(xi;µ2,Γ) t Γ =

(

1 00 1

)

.

sst♦♥ rs ss♦t t♦ ts ♠♦ s

r♠(xi) : zik =

1 (xi − µk)′(xi − µk) < (xi − µℓ)

′(xi − µℓ) t ℓ = 2− k0 ♦trs

s t ♣rtt♦♥ st♠t ② t ♠ ♠♦ s s s♥ t st Ω r♦♣st ♥s r t sst♦♥ rs sr s ♥♦t ♠sr qs t♦ ③r♦

Ω = xi ∈ R2 : r②s(xi) 6= r♠(xi).

♥tt② ♦ t ♠①tr ♠♦s

ss♥t ♦♥t♦♥ sss ♣r♦ ② str ♥②ss r ♥tr♣rttr♦♦t t ♣r♠trs ♦ t ♦♠♣♦♥♥ts t s s♦ r tt t ♣r♠trsr ♥q ♦r ① strt♦♥ s t♦ ♠♦s ♥ t s♠ strt♦♥♠st t s♠ ♣r♠trs s♦ rr t♦ t ♥tt② ♦ t ♠♦

Page 33: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

♥t♦♥ ♥tt② t t♦ ♠①tr ♠♦s ♥ t s♠ ♥tr♦r t ♦♠♣♦♥♥ts ♥ rs♣t② ♣r♠tr③ ② θ ♥ θ′ t♥ t ♠♦s ♥t

∀xi ∈ X p(xi;θ) = p(xi;θ′) ⇔ θ = θ′.

Pr♦♠ t♦ t r♥ ♦ t ♦♠♣♦♥♥ts ♦s② t ♠①tr♠♦s r ♥♦t strt② ♥t t ♦♥② ♣ t♦ s♣♣♥ s♥ t sss♥ r s strt ② t ♦♦♥ ①♠♣ ♦r ts s ♦ ♥♦♥♥tt② s ♥♦t sr r s♥ t ♥tr♣rtt♦♥ ♦ t ♣rtt♦♥ st②s♥t

①♠♣ ♥ ♦ t ♦♠♣♦♥♥ts t t ♦♠♣♦♥♥t ♠①tr ♠♦s ♣r♠tr③ ② θ = (π1, π2,α1,α2) ♥ θ′ = (π2, π1,α2,α1) t♥ ♦t ♣r♠tr sts ♥ t s♠ strt♦♥

∀xi ∈ X , p(xi;θ) = π1p(xi;α1) + π2p(xi;α2)

= π2p(xi;α2) + π1p(xi;α1)

= p(xi;θ′).

♥ ♦ t ♦♠♣♦♥♥ts ♥ ♥r♥ ♦t tt ts ♦♥t♦♥ ♦ ♥♦♥♥tt② ♦s ♥♦t str t ♠ ♦rt♠ s ♥①t st♦♥ t ♥ ②s♥r♠♦r t ♥ ♥♦ t st♥ ♣♥♦♠♥♦♥ ❬t❪

❲② ♥t ♠①tr ♠♦s ♥ ♦rr t♦ ♦ t ♣r♦♠ t♦t ♦♠♣♦♥♥t r♥ t ♥♦t♦♥ ♦ ♥tt② s ♥tr♦ ♦r t♠①tr ♠♦s ❬❪

♥t♦♥ ❲ ♥tt② ♠①tr ♠♦ ♥ p(xi;θ) s ♣s ② ♥t ♥

∀xi ∈ X p(xi;θ) = p(xi;θ′) ⇔ θ ♥ θ′ r q♥t

♥② ♠①tr ♠♦s r ② ♥t ♠♦♥ t♠ ♦♥ ♥ t t ♥t♠①trs ♦ ss♥ strt♦♥s t ♥t ♠①trs ♦ ♠♠ strt♦♥s ♥t ♥t ♠①trs ♦ P♦ss♦♥ strt♦♥s r ♠♥ rts st② t ♥tt② ♦ t ♠①tr ♠♦s ❬ ❨❪ ♥ ♦rr t♦ ♥ ♦ trrs♦♥♥ ♣rs♥t t t♦r♠ ♦ r ❬❪ ♠♦♥strts t ♥tt② ♦ s♦♠ ♥rt ♠①tr ♠♦s ♦r ♥st♥ t ♥rtss♥ ♠①tr ♠♦

♦r♠ ♦♥t♦♥s ♦ ♥tt② ❬❪ t P = P ♠②♦ ♦♥♠♥s♦♥ ♠t strt♦♥ ♥t♦♥s t tr♥s♦r♠s ψ(t) ♥♦r t ∈ Sψ t ♦♠♥ ♦ ♥t♦♥ ♦ ψ s tt t ♠♣♣♥ M : P 7→ ψs ♥r ♥ ♦♥t♦♦♥ ♣♣♦s tt tr ①sts t♦t ♦rr♥ () ♦ P stt F1 ≺ F2 ♠♣s Sψ1 ⊆ Sψ2 t ①st♥ ♦ s♦♠ t1 ∈ Sψ1 t1 ♥♥♣♥♥t ♦ ψ2 s tt lim

t→t1ψ2(t)/ψ1(t) = 0 ♥ t ss ♦ ♥t

♠①trs ♦ P s ② ♥t

Page 34: Model-based clustering for categorical and mixed data sets

Pr♠tr st♠t♦♥

Pr♦♣♦st♦♥ ♥tt② ♦ t ♥rt ss♥ ♠①tr ♠♦ ❬❪ ss ♦ ♥t ♠①trs ♦ ♥rt ss♥ strt♦♥s s ② ♥t

Pr♦♦ t Φ1(.;µ, σ2) ♥♦t t ss♥ ♠t strt♦♥ ♥t♦♥ t

♠♥ µ ♥ r♥ σ2 > 0 ts tr ♣ tr♥s♦r♠ s ♥ ② ψ(t) =exp(σ2t2/2−µt) rr t ♠② ①♦r♣② ② Φ1(xi;µ1, σ

21) ≺ Φ1(xi;µ2, σ

22)

σ1 > σ2 ♦r σ1 = σ2 t µ1 < µ2 ♥ ♦r♠ ♣♣s t Sψ =(−∞,∞) ♥ t1 = +∞

♦r s♦♠ ♠①tr ♠♦s r ♥♦t ♥t t r ♦ ♥trst s♥ t②♣r♦ ♠♥♥ rsts ♥ ♣rt ♥ s♥ tr ♣r♠trs s♠ ♥t

♥r ♥tt② s t ♥tt② ♦♥t♦♥ ♦ t♦♦ str♥♥t ss rstrt ♦♥t♦♥ ♥♠ ♥r ♥tt② s ♥tr♦

♥t♦♥ ♥r ♥tt② ♠♦ s ♥r② ♥t ♥t ♣r♠tr s♣ r t ♠♦ s ♥♦t ♥t ♣ t♦ t ♦♠♣♦♥♥t r♥ s ♠sr q t♦ ③r♦

s ♦♥ t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ ♠♥ ts ♥ ♦s ❬❪ ♥ s♥t ♦♥t♦♥ ♦ t ♥r ♥tt② ♦rt ♠①tr ♦ ♠t♥♦♠ strt♦♥s s ♠♦ s st ♥ Prt rts ♦♥ t ♣r♦♦ ♦ ts ♥r ♥tt② r ♥ ♦r s♦♠ ♠①tr♠♦s r ♥♦t ♥r② ♥t s♦ tr ♣r♠trs ♥♥♦t ♥tr♣rt sstrt ② t ♦♦♥ ①♠♣

①♠♣ ♦♥♥r ♥tt② ♦ t ♠①tr ♦ ♥♦r♠ strt♦♥st xi ∈ [a1; b2] r♥ ② t ♦♠♣♦♥♥t ♠①tr ♠♦ ♦ ♥♦r♠ strt♦♥s♦s t ♣ s

p(xi;θ) = πU [a1, b1] + (1− π)U [a2, b2],

r θ = (π, a1, b1, a2, b2) U [., .] ♥♦ts t ♣ ♦ ♥♦r♠ strt♦♥ ♥r a1 < b1 a2 < b2 a1 < a2 b1 < b2 ♥ a2 < b1 s ♠♦ s q♥t t♦t ♦♦♥ tr♦♠♣♦♥♥t ♠①tr ♠♦ ♦ ♥♦r♠ strt♦♥s ♦s t ♣ s

p(xi;θ′) = ε1U [a1, a2] + ε2U [a2, b1] + (1− ε1 − ε2)U [b1, b2],

r θ′ = (ε1, ε2, a1, a2, b1, b2) ε1 = π a2−a1b1−a1

♥ ε2 = π b1−a2b1−a1

+ (1− π) b1−a2b2−a2

s

t strt♦♥ ♦ xi ♥ ♠♦③ t t♦ r♥t ♣r♠tr③t♦♥s θ ♥ θ′

Pr♠tr st♠t♦♥

trtr ♦ ts st♦♥ str♥ ♦ t st ② ♥t ♠①tr ♠♦rqrs t st♠t♦♥ ♦ t ♠♦ ♣r♠trs ♥ ts st♦♥ ♣rs♥t tt♦ ♠♦st ♣♦♣r st♠ts ♥ t ♠①tr ♠♦ ♦♥t①t t ♠①♠♠ ♦♦st♠t rtr ♥♦t ② ♠ ♥ t ♠①♠♠ ♣♦str♦r st♠t rtr♥♦t ② ♠♣ ♦r ♦t st♠ts ♣rs♥t t st♠t♦♥ ♦rt♠s ♥tr trs s♣ t♦ t ♠①tr ♠♦s

Page 35: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

♥♥♥ ①♠♣ ♥ ts st♦♥ ♥t♦♥s ♥ ♦rt♠s r ♥ ♥ ♥r③ s ♥ t② r strt ② t ♦♦♥ r♥♥♥ ①♠♣ ①trt r♦♠t rt ②s♥ ♦♥ ♥ ♥r♥ ♦♥ ①trs ♦ strt♦♥s ♦ r♥ ♥rs♥ ♥ P ♦rt ❬❪

t xi ∈ R ♥ t t ♦♠♣♦♥♥t ♥rt ss♥ ♠①tr ♠♦♦s t ♣ s rtt♥ s

p(xi;θ) =2∑

k=1

πkφ1(xi;µk, σ2k),

r θ = (π1, µ1, σ21, π2, µ2, σ

22) ♥ r φ1(.;µk, σ

2k) ♥♦ts t ♣ ♦

t ♥rt ss♥ r N1(µk, σ2k)

♥♥♥ ①♠♣ ♦♠♣♦♥♥t ♥rt ss♥ ♠①tr

①♠♠ ♦♦ st♠t♦♥

♥ ts st♦♥ ♥ t ♦♦ ♥t♦♥ ♥ t st♠t ♦ t ♠①♠♠♦♦ ♦s sr t ♠♥ ♣r♦♣rts ♥ ♥tr♦ t ♥♦t♦♥♦ ♦♠♣tt ♥ t ♦♦ ♥t♦♥ ss♦t t♦ t ♥♠ ♦♠♣tt♦♦ ♥t♦♥

①♠♠ ♦♦ st♠t

♥ ♦♦ ♥t♦♥ ♦s t ♦ ♥♦r♠t♦♥ ♦♥t♥ ♥ tt st ♦r s t s ♠♦r ♦♠♦rt t♦ ♦r t t ♦rt♠ ♦ ts♥t♦♥ ♣rs♥t t ♥t♦♥s ♦ ♦t ♥t♦♥s

♥t♦♥ ♦♦ ♥t♦♥ ♦r ♥ s♠♣ x ts ♥t♦♥ ♦♠♣t t t ♣♦♥t θ s ♥ ② p(x;θ) =

∏ni=1 p(xi;θ)

♥t♦♥ ♦♦♦ ♥t♦♥ ♦♦♦ ♥t♦♥ ♦♠♣tr♦♠ t s♠♣ x ♥ t ♦♥ t ♣♦♥t θ s ♥ ②

L(θ;x) =n∑

i=1

ln p(xi;θ).

♦r ♥ s♠♣ x ♦♠♣♦s t n ♥s xi ∈ R t♥ t ♦♦ ♥ t ♦♦♦ ♥t♦♥s r ♥ s

p(x;θ) =n∏

i=1

2∑

k=1

πkφ1(xi;µk, σ2k) ♥ L(θ;x) =

n∑

i=1

ln2∑

k=1

πkφ1(xi;µk, σ2k).

♥♥♥ ①♠♣ ♦♦ ♥ ♦♦♦ ♥t♦♥s

Page 36: Model-based clustering for categorical and mixed data sets

Pr♠tr st♠t♦♥

♥ rq♥tst r♠♦r ♥t t♦ ♥r ♦r♥ t♦ t ♥♦r♠t♦♥ ♥② t t ♦ ♥tr ♣♣r♦ s t♦ sr t ♠①♠♠ ♦♦ st♠t♠ ♥♦t ② θ

♥t♦♥ ♠ ♠①♠♠ ♦♦ st♠t s ♥ ②

θ = argmaxθ

L(θ;x).

s t ♦♦♦ ♥t♦♥ ♦r s♠r② t ♦♦ ♥t♦♥ s tr♥t ts ♦♥t♦♥ s ♥r② r ♦r t ♠①tr ♠♦s t♠ s ♦t♥ ② s♦♥ t qt♦♥s ♥♥ ♦ t r♥t ♥ ♥♦♥ ♣♦st ♥t ss♥ ♠tr①

∇L(θ;x) = 0.

Pr♦♣rts ♦ t ♠ ♠ s ♣♦♣r st♠t s♥ t s ♥r rstrt ♦♥t♦♥s ♦♦ ♣r♦♣rts

t s ♥q t ♣r♦t② t♥♥ t♦ s t s♠♣ s③ r♦s t♦ ♥♥t② t s ♦♥sst♥t t s s②♠♣t♦t② ♥s t s s②♠♣t♦t② ss♥ t s②♠♣t♦t② ♠♥♠③s t r r♥

ts ♦♥ t ♦♥t♦♥s ♥♦♥ ts ♣r♦♣rts r ♥ ♥ ♦r② ♦ P♦♥tst♠t♦♥ ♣tr ② ♠♥♥ ♥ s ❬❪ s t ♠ s♦♦ ♣r♦♣rts t s ♥tr t♦ st② ts ①st♥ ♥ t ①sts t ♠t♦s♣r♦r♠♥ ts st♠t♦♥

♥r② ①st♥ ♥ t ♥q♥ss ♦ t ♠ s ♥♦t r♥t ♦rt ♠①tr ♠♦s ♥ t ♦♦♦ ♥t♦♥ ♥ ♥♦t ♣♣r ♦♥s t r♥♥♥ ①♠♣ ♥ s s t ♦♦ ♥t♦♥ ♥ t♥ t♦ t♥♥t② s stt♦♥ ♥♠ ♥r② ❬❪ ♥♦s ♥♦♥sst♥t st♠t♦rs♥ s s t st♠t♦r r②♥ ♥ ♥♦♥ ♥t ♦♦♦ ss♦ sr

❲ ♥t t♦ t ♦♠♣♦♥♥t ♥rt ss♥ ♠①tr ♠♦ ♦♥ ts♠♣ x ss♠ tt µ1 = x1 ♦sr ♠♦ ♥r② s♥

limσ21 → 0σ22 > 0

L(θ;x) = ∞.

♥♥♥ ①♠♣ ♥r②

♥t♦♥ ♠ ♥ ♥♦♥ ♦♦♦ ❲♥ t ♦♦♦♥t♦♥ s ♥♦t ♣♣r ♦♥ t ♠ s ♥ s

θ = argmaxθ∈θ:L(θ;x)<+∞ ♥ ∇L(θ;x)=0

L(θ;x).

Page 37: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

♦t tt t ♦♦♦ ♥t♦♥ s ♥r② sr ♦ ♦♣t♠ ♥rs t t② ♥ ♥♥ t ♠ s ♣♥♦♠♥♦♥ s ♥♦ strt ♦♥t r♥♥♥ ①♠♣

❲ ♥rt s♠♣ ♦ s③ r♦♠ t ♦♠♣♦♥♥t ♥rt ♠①tr♠♦ ♦s t ♣r♠trs r ♥ s ♦♦s

π1 = 1/3, π2 = 2/3, µ1 = −1, µ2 = 3.5 ♥ σ21 = σ2

2 = 1.

❲ ss♠ tt t ♣r♦♣♦rt♦♥s ♥ t r♥s r ♥♦♥ ♥ ♥tt♦ st♠t t ♠♥s ② ♠①♠♠ ♦♦ r s♣②s t ♦♦♦ ♥t♦♥ ♦r♥ t♦ t s ♦♥ ♦t ♣r♠trs ♥ ♥♦sr tt ts ♥t♦♥ s t♦ ♦♣t♠ ♦ ♦♥ s ♦t r♦♥(−1, 3.5) t ♦ ♦♥ s ♦t r♦♥ (3.5,−1)

♥♥♥ ①♠♣ ♦♦♦ ♦♣t♠ t ♥♥♦♥ ♠♥s

−4 −2 0 2 4 6

−4

−2

02

4

µ1

µ 2

r ♦♦♦ s ♦r t s♠♣ ♦ s③ ♦r♥ t♦ t s♦ (µ1, µ2)

♦ ①♣t s♦t♦♥ ♦r t ♠①tr ♠♦s t sr ♦ t ♠ ♥♦st♦ s♦ qt♦♥s ♥ ♥♦ ♥②t s♦t♦♥ rt ♦♠♣tt♦♥ ♦ t♠ s ♥♦t s② s ♦ t s♣ ♦r♠ ♦ t ♦♦♦ ♥t♦♥ s♠ ♦♦rt♠s ♦ s♠s ♦ ♣ ♦ s♦♠ trt ♣r♦rs s♦ s t t♦♥♣s♦♥ ♦rt♠ t ♦r ♥st♥ ♥ ♠r ♦♣t♠③t♦♥t♦rt ♥ ♣rt s♣ts ② ♦♥♥♥s rt ♠r ♥ st③ ❬❪ ♦r ts ♠♣♠♥tt♦♥ s ♦t♥ ♦♠♣① s♥ t♥♦s t ♦♠♣tt♦♥ ♦ t rts ♦ t ♦♦

Page 38: Model-based clustering for categorical and mixed data sets

Pr♠tr st♠t♦♥

♠①tr ♠♦s ♥ ss♠♥t s ♦ t ♥♥t♦♥ ♦ t ♠♦rt♠ ♦s t ♠♣♠♥tt♦♥ s s♠♣ s♥ ♥♦ rt ♦ t ♦♦s ♥♦ s ts ♦rt♠ s s♣③ ♦r t ♠ss♥ t rst② ♥t ♥♦t♦♥ ♦ ♦♠♣tt ♦r t ♠①tr ♠♦ ♥ s♦♥② t t

sr t ♥ ♦♠♣tt ♦r s♠♣ x t ♥rt ♠♦s ss♠ tt t r♥ ♦ ♥ xi ♥♦s t ♣r♠♥r② s♠♣♥ ♦zi s Prr♣ ♥rt ♠♦ ♥ t♦♥ s t♦r z s ♥♦srt s ♦♥sr s ♠ss♥ s x s ♥♠ t ♦sr t t♦♣ (x, z) s ♥♠ t ♦♠♣tt ♥ t s♠ ② t ♦♦♦ ♥t♦♥ ♦♠♣t ♦♥ (x, z) s ♥♠ t ♦♠♣tt ♦♦♦ ♥t♦♥ ♥ t srtt♥ s ♦♦s

L(θ;x, z) =n∑

i=1

ln p(xi, zi;θ)

=n∑

i=1

ln

(

g∏

k=1

(πkp(xi;αk))zik

)

=n∑

i=1

g∑

k=1

zik ln (πkp(xi;αk)) ,

② strt♥ r♦♠ t rt♦♥ t♥ t ♣ p(xi;θ)p(zi|xi;θ) = p(xi, zi;θ) ♦♥♥ t ♦♦♥ rt♦♥

L(θ;x) = L(θ;x, z) + e(z,x;θ).

r e(z,x;θ) = −∑ni=1

∑gk=1 zik ln tik(θ) s L(θ;x) ≥ L(θ;x, z) s♥ tik(θ)

s ♣r♦t②

♦r t ♦♠♣♦♥♥t ♥rt ss♥ ♠①tr ♠♦ t ♦♠♣tt ♦♦♦ ♥t♦♥ ♦♠♣t ♦♥ θ ♦r t s♠♣ x ♥ t ♣rtt♦♥ z s ♥ s

L(θ;x, z) =2∑

k=1

nk lnπkσk

− 1

2

2∑

k=1

n∑

i=1

zik(xi − µk)

2

σ2k

− n ln√2π.

♥♥♥ ①♠♣ ♦♠♣tt ♦♦♦

♦rt♠s ♦r ♠①♠♠ ♦♦ st♠t♦♥

♠ ♦ t ♠①tr ♠♦s s ♥r② ♦t♥ ♥ ♠ ♦rt♠ sst♦♥ s ♦t t♦ t ♣rs♥tt♦♥ ♦ ts ♦rt♠ ♥ ♦ ts ①t♥s♦♥s

Page 39: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

♠ ♦rt♠

Prs♥tt♦♥ ♦ t ♠ ♦rt♠ ①♣tt♦♥①♠③t♦♥ ♦rt♠rtr ♥♦t ② ♠ s ♣r♦♣♦s ② ♠♣str r ♥ ♥ ♥ ❬❪ ts ♦♠♥s ♦ ♣♣t♦♥ r str t♥ t ♠①tr ♠♦s s♥ ts s♣③ ♥ t s ♦ ♠ss♥ s ♥ t ♦♥t①t ♦ t ♠①tr ♠♦st ss ♠♠rs♣s ♦ t ♥s r ♥tr♣rt s ♠ss♥ s ♦ttt ts ♦rt♠ ♦s t♦ str t st t ♠ss♥ s ② ♠♥② rstrt ss♠♣t♦♥s ♠♥ ♥t ♦ ts ♦rt♠ s ts s♠♣t② s♥ t ♦♣t♠③s t ♦♦ ♥t♦♥ t♦t ♦♠♣t♥ ts rtsrtr♠♦r s♥ ts ♠♣♠♥tt♦♥ ♥ ♣r③ t st②s ♥t ♥ tss ♦♥r♦♥t t r t sts s st♦♥ s st ♥ ♦r ♦ ts ♦rt♠t rr ♥♥ ♠♦r ts ♦ rr t♦ ♦rt♠ ♥ ①t♥s♦♥s ② ♥ ♥ rs♥♥ ❬❪

♥t♦♥ ♦ t ♠ ♦rt♠ ♠ ♦rt♠ s ♥ trt ♦♥ strt♥r♦♠ ♥ ♥t ♦ t ♣r♠tr tr♥ts t♥ t t♦ ♦♦♥ st♣s t ♦♠♣tt♦♥ ♦ t ①♣tt♦♥ ♦ t ♦♠♣tt ♦♦♦ st♣ ♥ ts ♠①♠③t♦♥ ♠ st♣

trt♥ r♦♠ ♥ ♥t θ[0] trt♦♥ [r] s rtt♥ s st♣ t Q(θ;θ[r]) r

Q(θ;θ[r]) = Eθ[r] [L(θ;x, z)] ,

st♣ st θ[r+1] s s

θ[r+1] = argmaxθ

Q(θ;θ[r]).

♦rt♠ ♠ ♦rt♠

t♦♣♣♥ rtr ♦ rtr r ♥r② s ♠♦st ♦♠♠♦♥ ♦♥ ♦♥ssts♥ st♦♣♣♥ t ♦rt♠ ♥ t ♥rs ♦ t ♦♦♦ s ♦r t♥ ttrs♦ ε ♦s♥ ② t sr s♦ ♥

L(θ[r+1];x)− L(θ[r];x) < ε.

s♦♥ ♦♥ ①s ♥ ♥ t ♥♠r ♦ trt♦♥s ♣r♦r♠ ② t ♦rt♠

♦s t ts ♥r♥t t♦ t ♠①tr strtr st♠t♦♥ ♦ t ♣r♠trs s ♦ ♦r ♠①tr ♠♦ t♦t ♦♥str♥t t♥sss t ♥r♥ ♦ s ♠♦ ♥ ♠ ♥ t ss ♠♠rs♣s ♦t ♥s r ♥♦♥ s ♠①tr ♠♦ ♦s t st♠t s trt ♥t sr♠♥♥t ♥②ss s♦ ♥ t s r ♥♦♥ ♥ ②s ♥rr♥ str♥ ♣r♦♠ ♦r ♥st♥ t ♠①tr ♠♦s ♦s t ♦♠♣♦♥♥t

Page 40: Model-based clustering for categorical and mixed data sets

Pr♠tr st♠t♦♥

strt♦♥s ♦♥ t♦ t ①♣♦♥♥t ♠② ♥ ♥ ♥♦ ♦♥str♥t t♦tr ♥ ①♣t② ♦♠♣t

❲ ♦♥sr t ss ss♥ ♠①tr ♠♦ ♦s t ♦♠♣♦♥♥tsr ♥ ② trt♦♥ [r] ♦ t ♠ ♦rt♠ s rtt♥ s

st♣ t ♦♥t♦♥ ♣r♦ts

tik(θ[r]) =

π[r]k p(xi;α

[r]k )

p(xi;θ[r])

.

st♣ ♠①♠③t♦♥ ♦ t ①♣tt♦♥ ♦ t ♦♠♣tt ♦♦♦

π[r+1]k =

n[r]k

n, µ

[r+1]k =

1

n[r]k

n∑

i=1

tik(θ[r])xi,

σ2[r+1]k =

1

n[r]k

n∑

i=1

tik(θ[r])(xi − µ

[r+1]k )2,

r n[r]k =

∑ni=1 tik(θ

[r])

♥♥♥ ①♠♣ ♠ ♦rt♠

Pr♦♣rts ♦ t ♠ ♦rt♠ ❯♥r rstrt ss♠♣t♦♥s ❬❲❪ t♠ ♦rt♠ ♣r♦s sq♥ ♦ st♠ts θ[r] ♦♥rs t♦ ♦ ♦♣t♠♠ ♦ t ♦♦♦ ♥t♦♥ s ♦♣t♠♠ ♦♥② ♣♥s ♦♥ t ♥t③t♦♥θ[0] ♥ t ♦♦ ♥t♦♥ ♥rss t trt♦♥ ♦ t ♠ ♦rt♠

∀[r], L(θ[r+1];x) ≥ L(θ[r];x).

s ♦rt♠ ♦♥rs t♦ ♦ ♦♣t♠♠ ♦ t s ♠♥t♦r② t♦ ♣r♦r♠ srr♥t ♥t③t♦♥s ♥ ♦rr t♦ ♦♣ t♦ t t ♠ ♥ ♦tr r ♦ t♠ ♦rt♠ s ts s♣ ♦ ♦♥r♥ ♥ ts ♦rt♠ ♥ ♦♥r s♦②s♣② ♥ t sss r ♦r♣♣ s ♠♥② t♦rs ♥ ♥trst♥ t rt♦♥ ♦ t ♠ ♦rt♠ s ♦r ♥st♥ ❬❱ ❪ tr tsr♣t♦♥ ♦ t ♠ ♦rt♠ ♣♣ ♦♥ t ss♥ ♠①tr ♠♦s ♣rs♥ttr ♦ ts ①t♥s♦♥s r♥ ts rs

Page 41: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

❲ ♦♥sr t ♦♠♣♦♥♥t ♥rt ss♥ ♠①tr ♠♦ t♥♦♥ ♣r♦♣♦rt♦♥s ♥ r♥s ♥ s s t ♠ st♣ ♦♥② ♦♥ssts ♥ ♦♠♣t♥ µ[r+1]

k s♥ t ♦tr ♣r♠trs r ♥♦♥ r s♣②s t s ♦ t ♦♦ ♦♠♣t t trt♦♥s ♦ t♦r♥s ♦ t ♠ ♦rt♠ r♥ ♣r♥t t tr♥s s ♥t③ t(−1,−1/2) ♥ ♦♥rs t♦ t ♠ t r♥ ♣r♥t t sqrss ♥t③ t (3.5, 3) ♥ ♦♥rs t♦ ♦ ♠①♠♠ ♦ t ♦♦♥t♦♥

♥♥♥ ①♠♣ ①♠♣ ♥ ♠ ♦rt♠

−4 −2 0 2 4 6

−4

−2

02

4

µ1

µ 2

r ♦♦♦ s ss♦t t t♦ sq♥s ♦ ♣r♠trs ♣r♦♥ ② ♥ ♠ ♦rt♠

①t♥s♦♥s ♦ t ♦rt♠

♦rt♠ ♦♠t♠s t s♦t♦♥ ♦ t ♠ st♣ s ♥♦t ①♣t ♥ s s t ♥r③♠ ♠ ♦rt♠ ♥ s ♠ st♣ s s♦ r♣② ♠ ♦♥ ♦♥② rqrs t ♥rs ♦ t ①♣tt♦♥ ♦ t ♦♠♣tt ♦♦♦ s t trt♦♥ [r] t st♣ s ♥♥ t ♠st♣ tr♠♥s θ[r+1] s s

Q(θ[r+1];θ[r]) ≥ Q(θ[r];θ[r]).

♠ ♦rt♠ ♣s t ♠♦♥♦t♦♥ ♣r♦♣rt② ♦ t ♥rs ♦ t ♦♦♥t♦♥ ♦r trt♦♥ s ♥rt r♦♠ t ♠ ♦rt♠ ♦r ts♦rt♠ rqrs ♠♦r trt♦♥s t♥ t ♠ s♥ ts ♦♥r♥ s s♦r

Page 42: Model-based clustering for categorical and mixed data sets

Pr♠tr st♠t♦♥

♥ ♦rt♠s ♥ ♦rr t♦ ♦r♦♠ t tr ♠♥ rs♦ t ♠ ♦rt♠ str♦♥ ♣♥♥② t t ♥t③t♦♥ ♣♦♥t ♦ ♦♣t♠♠ ♦♥r♥ ♥ s♦ ♦♥r♥ t t♦st♠ s♠ ♦rt♠ s♣r♦♣♦s ❬+❪ ♦rt♠ ♥♦r♣♦rts st♦st st♣ s st♣ t♥t st♣ ♥ t ♠ st♣ rt ② t r♥♦♠ ♠♣tt♦♥ ♣r♥♣ sq♥ ♥rt ② t s♠ ♦rt♠ ♦♥rs t♦ ♥q stt♦♥r② strt♦♥♦s t♦ p(θ|①)

trt♥ r♦♠ ♥ ♥t θ[0] trt♦♥ [r] s rtt♥ s st♣ t t ♦♥t♦♥ ♣r♦ts

tik(θ[r]) =

π[r]k p(xi;α

[r]k )

p(xi;θ[r])

.

st♣ s♠♣ t ss ♠♠rs♣ s s

z[r]i ∼ M(ti1(θ

[r]), . . . , tig(θ[r])).

st♣ st θ[r+1] s s

θ[r+1] = argmaxθ

L(θ;x, z[r]).

♦rt♠ s♠ ♦rt♠ ♦r t ♠①tr ♠♦s

♦rt♠ s st♦♣♣ tr ♥♠r ♦ trt♦♥s ♦s♥ ② t sr ♦ttt ♥♦tr rs♦♥ ♦ ts ♦rt♠ ♥♠ s♠ ♣r♦s ♥ ♠♦st sr②♦♥r♥ t♦ t ♥q stt♦♥r② strt♦♥ ❬❪ t s tr ♦ t♥ rs♦♥ s♠t ♥♥♥ ♠ ♦rt♠ ♥ t s♠ ♦rt♠ ♥ t st♣ ♥♥♥ s ♥tr♦ tr t s st♣ ♥ ♦rr t♦ r t ♠♣t ♦ tr♥♦♠ ♣rtrt♦♥s ♣r♦r♠ ② t s st♣ s rt♦♥ ♥rss t t♥♠r ♦ trt♦♥s s ♥ t s♠ strts t ♦rs t s♠ ♦rt♠t♥ t t♥s t♦ t ♠ ♦rt♠ ♥ t ♥♠r ♦ trt♦♥s ♥rss

♦rt♠ sst♦♥♠ ♠ ♦rt♠ ❬❪ s ♥r ♦rt♠ t♦ ♦♠♣t t st♠t ♥ t♦ ♥ t ♣rtt♦♥ ♥r t sst♦♥♣♣r♦ s t ♣r♦s t ♦♣ (θ, z) ♠①♠③♥ t ♦♠♣tt ♦♦♦

argmax(θ,z)

L(θ;x, z).

♥ t st♠t ♦ t ♠①♠♠ ♦♠♣tt s s ♥ ♥♦♥sst♥t❬♦❪ ts rsts ♥ ttr t♥ t♦s ♦ t ♠ ♥ t s♠♣ s③ ss♠ ♥ ♥ t sss r s♣rt rtr♠♦r t ♠ ♦rt♠♦♥rs str t♥ t ♠ ♦rt♠ ♠ ♦rt♠ ♥♦r♣♦rts sst♦♥ st♣ t♥ t st♣ ♥ t ♠ st♣ ♦r♥ t♦ t ♠♣ ♣r♥♣s ts ♦♥r♥ s♣ s ①♣t t♦ str t♥ t ♦♥r♥ s♣ ♦t ♠ ♦rt♠

Page 43: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

trt♥ r♦♠ ♥ ♥t θ[0] ts trt♦♥ [r] s rtt♥ s st♣ t Q(θ;θ[r]) r

tik(θ[r]) =

π[r]k p(xi;α

[r]k )

p(xi;θ[r])

.

st♣ ♠♥♠③ e(z,x;θ[r]) s♦

z[r]ik =

1 tik(θ[r]) ≥ tiℓ(θ

[r]) ∀ℓ = 1, . . . , g0 ♦trs

st♣ st θ[r+1] s s

θ[r+1] = argmaxθ

L(θ;x, z[r]).

♦rt♠ ♠ ♦rt♠ ♦r t ♠①tr ♠♦s

♠r ♠ ♦r t s♣r ss♥ ♠①tr ♠♦ ♥ ♠♥s ♦rt♠ ♠♥s ♦rt♠ s q♥t t♦ t ♠ ♦♥ ♥ t ♠♦ t♥ s t s♣r ss♥ ♠①tr ♠♦ t q ♣r♦♣♦rt♦♥s ❬❪

rs ♦ t ♠①♠♠ ♦♦ ♣♣r♦s ♥ t ①t♥s♦♥s♦ t ♠ ♦rt♠ r ts ♠♥ rs tr ♣r♦♠s st② ♥r♥t t♦ t♠①♠♠ ♦♦ ♣♣r♦ ♣♣ ♦♥ ♠①tr ♠♦s

rst ♦♥ s t t② t♦ ♥ t ♦ ♠①♠♠ ♦ t ♦♦♥t♦♥ ♦t tt ts ♣r♦♠ s ♠♦r ♣rs♥t ♥ t s♠♣s r s♠s♥ t ♦♦ ♥t♦♥ ♥ r② ♠♣②

s♦♥ ♦♥ s t♦ t ♣♣r♦♥ ♦ t ♦♦ ♥t♦♥ ♥ ts ♥t♦♥ s ♣♣r♦♥ ♦r t♦r t t ♥ ♣♣r♥♦♥ ♥ ♦tr stt♦♥s s ♦r ♥st♥ t tr♦sst ss♥♠①tr ♠♦ s t st♠t rtr♥ ② t ♦rt♠ ♥ ♦♥ t♥r② ② ♥ s s ts st♠t s s♦ s ♥ ♥♦♥sst♥t

tr ♦♥ s ♦t t rrt② ♦♥t♦♥s r ♦t♥ ♦t ♦rt s♠ t sts s t st♠t♦♥ ♥ ♥♦ ♥ ♦rtt♥

①♠♠ ♣♦str♦r st♠t♦♥

②s♥ r♠♦r ♥ t ②s♥ r♠♦r t ♣r♠tr θ s ss♠ t♦ ts r♥♦♠ r ♦s t ♣r♦r strt♦♥ s ♥♦t ② p(θ) sstrt♦♥ ♦♥t♥s t ♥♦r♠t♦♥ ♦♥ θ ♥ ② ♥ ①♣rt s t tr♠♣r♦r ♥ ♥tr♣rt s ♦r t♦ ♦sr t t r r r♥t ♣r♦rstrt♦♥s ♥ tr ♠♣t ♦♥ t ♥r♥ ♥ ♥♦t ♥ s♣② ♦rt s♠ t sts s strt♦♥s r ♣rs♥t ♥ ②s♥ ♦ ②P ♦rt ❬♦❪ ♥ r ts ♦♥ t ②s♥ r♠♦r t ♣r♦rstrt♦♥ ♦♥t♥s t ♥♦r♠t♦♥ ♥ ② ♥ ①♣rt t strt♦♥ r♦♠

Page 44: Model-based clustering for categorical and mixed data sets

Pr♠tr st♠t♦♥

♥r♥s r ♠ s t ♣♦str♦r ♦♥ ♣♦str♦r strt♦♥ ♦♥t♥s t♣r♦r ♥♦r♠t♦♥ ♥ ② t ①♣rt p(θ) ♥ ② t t (x) s t tr♠♣♦str♦r ♥ ♥tr♣rt s tr t♦ ♦sr t t

P♦str♦r strt♦♥ ♥ ♦♦ ♥t♦♥ ②s r ♥♦s ttt ♣♦str♦r strt♦♥ p(θ|x) s ♥ s

p(θ|x) = p(x|θ)p(θ)p(x)

.

♦t tt t ♥♦r♠t♦♥ ♥ ② t t s rt t♦ t ♦♦ ♥t♦♥p(x|θ) s t ♥t♦♥ ♦ t ♦♦ ♥t♦♥ s r s♥ ts ♥t♦♥♦♥t♥s t ♥♦r♠t♦♥s ♥ ② t t ♥ ♦t rq♥tst ♥ ②s♥r♠♦rs s ♥t♦♥ s s♦ ♦♠♠♦♥ s ♦r ♦t ♦♠♠♥ts ♥p(x) s ♦♥st♥t ♦r♥ t♦ θ t ♦♦♥ rt♦♥ s s ♥ p(x) s ♥♦t♦♠♣t

p(θ|x) ∝ p(x|θ)p(θ).

♥ ♥ts ♦ t ②s♥ ♣♣r♦ ②s♥ ♣♣r♦s ♦rt ♠①tr ♠♦ r t ♥ ♥t ①tr ♥ r♦ t♥ ♦s ② rürt♥ttr ❬❪ r♦♠ r ♦♥ ♥ ①trt t ♦r ♦♦♥ ♠♥qts

♣r♦r s s♠♦♦t t ♦♥ t ♣r♦♠s ♦ t ♥rt s♦t♦♥

s ♠t♦s t ♥t♦ ♦♥t t ♣r♠tr ♥rt♥t② ② st② ♥ s r rrt② ♦♥t♦♥s r ♦t s♠ t

st ♠①tr t s♠ ♦♠♣♦♥♥t ♣r♦♣♦rt♦♥s s♥ t② ♦ ♥♦t r② ♥s②♠♣t♦t ♥♦r♠t②

r ♠♣♠♥tt♦♥ s ♥♦t ♦♠♣① ♥ t ♦♠♣♦♥♥t strt♦♥s ♦♥ t♦ t ①♣♦♥♥t ♠② ♥ ♥ s s t ♦♥t ♣r♦rstrt♦♥s ♣r♦ ①♣t ♣♦str♦r strt♦♥s ❬♦❪

❲ ♦♥sr t ♦♠♣♦♥♥t ♥rt ss♥ ♠①tr ♠♦ t♥♦♥ ♣r♦♣♦rt♦♥s ♥ r♥s ♥ ts s θ = (µ1, µ2) ss♠♥♣♥♥ t♥ t ♣r♦r strt♦♥s ♥ s t r②s ♥♦♥♥♦r♠t ♦♥s s♦

p(θ) = p(µ1)p(µ2) t µ1 ∼ N (ξ, κ) ♥ µ2 ∼ N (ξ, κ),

r ξ ♥ κ r ②♣r♣r♠trs r s♣②s t s ♦ t♣r♦r ♥ ♦ t ♣♦str♦r strt♦♥s ♦♠♣t ♦♥ s♠♣ ♦ s③

♥♥♥ ①♠♣ ②s♥ r♠♦r

Page 45: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

−4 −2 0 2 4 6

−4

−2

02

4

µ1

µ 2

♣r♦r strt♦♥

−4 −2 0 2 4 6

−4

−2

02

4

µ1

µ 2

♣♦str♦r strt♦♥

r Pr♦r ♥ ♣♦str♦r strt♦♥s ♦r ♦♠♣♦♥♥t ♥rt ♠①tr♠♦ t ξ = 1 ♥ κ = 9

♠♦♦t t ♥ ♥rt s♦t♦♥ ②s♥ r♠♦r ♥ ♦s♦♠ ♥r② ♣r♦♠s ② s♥ ♣r♦r strt♦♥s ♣r♦s s♠♦♦tt s strt ♦

r♦♠ t s♠♣ x rs♥ r♦♠ t ♦♠♣♦♥♥t ♥rt ss♥ ♠①tr ♠♦ t ♥♦♥ ♣r♦♣♦rt♦♥s t µ1 = 0 ♥ µ2 = x1 t ♠ st♦ ♥r t ♣r♠tr θ = (σ2

1, σ22) rq♥tst ② ♥ sr r♦♠

♥r② s♦t♦♥ s strt ♥ ♥♥♥ ①♠♣ ♥ ②s♥② ss ♥♣♥♥ ss♠♣t♦♥ t♥ ♣r♦r strt♦♥s ss♦t t ♦♥t ♣r♦r strt♦♥s ♥♦s tt

p(θ) = p(σ21)p(σ

22) r 1/σ2

1 ∼ G (c0, C0) ♥ 1/σ22 ∼ G (c0, C0) .

♥ t ♣♦str♦r strt♦♥ p(θ|x) =∑z∈Z p(θ|x, z)p(z|x) r Z =

1, 2n t♥ ts strt♦♥ s ♣♣r♦♥ ②

p(θ|x) ≤∑

z∈Z

p(θ|x, z) ♥ p(θ|x, z) = p(σ21|x, z)p(σ2

2|x, z).

♥ 1/σ2k|x, z ∼ G

(

c0 +♥k2, C0 +

i:zik=1(xi−µk)

2

2

)

♥ s♥ t ♠♦

♦ G(α, β) s α−1β

♥ α ≤ 1 t♥

p(θ|x) < +∞ ∀θ.

♥♥♥ ①♠♣ ♠♦♦t t ♥ ♥rt s♦t♦♥

♦t tt t ♦♥t ♣r♦r strt♦♥s r ♦t♥ s s♥ t② s♥♥t②s♠♣② t ♥r♥ ♦r t ♦ ♦ t ②♣r♣r♠trs ♣r♠trs ♦t ♣r♦r strt♦♥ ♥ t ♥ t r②s ♥♦♥ ♥♦r♠t ♣r♦r s

Page 46: Model-based clustering for categorical and mixed data sets

Pr♠tr st♠t♦♥

♥♦t s♦t♦♥ s s♦ t♦ ① t ②♣r♣r♠trs ② s♥ ♥ ♠♣r②s♥ ♣♣r♦ tr♠♥s t ②♣r♣r♠trs ♦r♥ t♦ t ts ♦r ♥st♥ ❬❪ ♦r t ss♥ ♠①tr ♠♦

②s♥ ♥r♥ ②s♥ ♣♣r♦s ♦t♥ ♥ s♠t♦♥ ♠t♦s r♦ ♥ ♦♥t r♦ ♠♠ t♦ ♥rr ♦ tr ♦♣♠♥t s r♥ts t s rt t♦ t ♦♠♣tt♦♥ ♣♦r ♥ t ♣♦str♦r strt♦♥♦♥t♥s t ♦ ♥♦r♠t♦♥ ♦t θ ♥♦r♠t♦♥ ♥ ② t ①♣rt ♥ ② tt ♥② ♥r♥ ♦♥ θ r s ♦♥ ts strt♦♥ t s s♦ ♥tr t♦ ♦♣tt ♣♣r♦ s♠r t♦ t ♦♥ s ♥ t rq♥tst r♠♦r ♦ ♥t t♦♦t♥ t st♠t ♦ t ♠①♠♠ ♣♦str♦r st♠t ♠♣ ♥♦t ② θ

♥t♦♥ ①♠♠ ♣♦str♦r st♠t ♠①♠♠ ♣♦str♦rst♠t s ♥ ②

θ = argmaxθ

p(θ|x) = argmaxθ

p(x|θ)p(θ).

♠r ♥ t♥ t ♠ ♥ ♠♣ ♦t tt t ♣r♦r ♦♦s ♥♦r♠ strt♦♥ t♥ p(x|θ) ∝ p(θ|x) ♥ s s t ♠ s s♦ q t♦t ♠♣

s t ♠♣ s rs♦♥ st♠t t t ♥ t t♦ ♦t♥ t♥ s♦ r♣ ② t ♠♥ ♦r t ♠♥ ♦ t ♣♦str♦r strt♦♥ ♦tttr st♠ts r ♠♦r s② ♦t♥ ♠♠ ♦rt♠s ❲ ♥♦ t t♠♥ ♦rt♠s ♣r♦r♠♥ t ②s♥ ♥r♥ ♦♥ θ

♦rt♠s ♦r ♠①♠♠ ♣♦str♦r st♠t♦♥

♦rt♠ ♦r ②s♥ st♠t♦♥

♥ ♠ ♦rt♠ ♥ ♠♦ t♦ ♣r♦ t♠♣ ♦r t st♠t♦ t ♠①♠♠ ♣♥③ ♦♦ ❬r❪ s ♦t s ② s♥ t♦♦♥ rt♦♥ ♦t♥ ② ♣♣②♥ t ②s r ♥ ② s♥ t ♦rt♠♥t♦♥

argmaxθ

p(θ|x) = argmaxθ

L(θ;x) + ln p(θ).

♦ ♦t♥ t ♠♣ t ♠ st♣ ♦ t ♠ ♦rt♠ ♦♥ssts ♥ t ♠①♠③t♦♥♦ t ①♣tt♦♥ ♦ t ♦♠♣t t ♣♦str♦r strt♦♥ p(θ|①, ③) t trt♦♥[r] t ♠ st♣ tr♠♥s t ♣r♠tr θ[r+1] s s tt

θ[r+1] = argmaxθ

Q(θ;θ[r]) + ln p(θ).

Page 47: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

t ♦♠♣♦♥♥t ♥rt ss♥ ♠①tr ♠♦ ♦s ♦♥② t ♠♥sr ♥♥♦♥ ♣r♦r strt♦♥ ♦ t ♣r♠trs s ♥ ♥ s♦ t ♠ st♣ s rtt♥ s

µ[r+1]k =

σ2kξ + κ

∑ni=1 tik(θ

[r])xi

σ2k + κn

[r]k

.

♥♥♥ ①♠♣ ②s♥ st♠t♦♥ ♦ ss♥ ♠①tr

♦rt♠s ♥ ②s♥ st♠t♦♥

trtr ♦ ts st♦♥ ❲ ♥♦ ♣rs♥t s♦rt ♦r ♦ tr ♠♥ ♦rt♠s s t♦ ♥r t ♣r♠trs ♦ ♠①tr ♠♦ t s s♠♣rt tr♦♣♦sst♥s ♦rt♠ ♥ t tr♦♣♦st♥s s♠♣r rr ♥t♥ ♠♦r ts ♥ r♣♦rt ♦♥ ♦♥t r♦ ttst t♦s ②P ♦rt ❬❪ s ♦rt♠s r ♠♠ ♦♥s ♦s t r♦ ♥s t ♣♦str♦r strt♦♥ p(θ, z|x) s t stt♦♥r② strt♦♥ s t②s♠♣ sq♥ ♦ ♣r♠trs ♦r♥ t♦ tr ♣♦str♦r strt♦♥ s♥ ts♣♣r♦ ♦s s t♦ ♣r♦r♠ t ②s♥ ♥r♥

♠r ♠♦sts♦r♥ stts ♥♦♥ r♥t ♥t③t♦♥s ♦ t♦rt♠s ♥ t ♠♠ ♦rt♠s ♥ ♥ rr ♥ r♦ r♦♥ r ♥♦t t♦rt② s♥st t♦ t ♦ ♦♣t♠ tr ♦r s ♥♦t s♦♣rt ♥ ♣rt s ♦r ♥st♥ ❬❪ ♥ tr r tr♣♣♥ stts r ♠♦sts♦r♥ stts rqr♥ s♦ r ♥♠r ♦ trt♦♥s t♦ s♣r♦♠ t♠ tt t ♦rt♠ s ♥r② st♦♣♣ ♦r

s s♠♣r

♥ s s♠♣r s t ♠♦st ♣♦♣r ♣♣r♦ t♦ ♣r♦r♠ t②s♥ ♥r♥ ♦ ♠①tr ♠♦ s♥ t ss t t♥t strtr ♦ t ts ♦rt♠ s t ♦♥ ♦♥t♦♥ strt♦♥s r♦♠ t s s② t♦s♠♣

s s♠♣r ♥ ♠①tr ♠♦s s s♠♣r s ♥ trt ♦rt♠ ♦s ♦♥ trt♦♥ s s♣t ♥ t♦ ♠♥ st♣s ♦r t ♠①tr ♠♦ r♠♦r♥ ts ♦rt♠ tr♥t② s♠♣s t ss ♠♠rs♣s ♦♥t♦♥② ♦♥t ♣r♠trs ♥ ♦♥ t t ♥ t ♣r♠trs ♦♥t♦♥② ♦♥ t ss ♠♠rs♣s ♥ ♦♥ t t s ts stt♦♥r② strt♦♥ s p(θ, z|x) tr♦r tsq♥s ♦ t ♥rt ♣r♠trs r s♠♣ r♦♠ tr ♣♦str♦r strt♦♥p(θ|x)

Page 48: Model-based clustering for categorical and mixed data sets

Pr♠tr st♠t♦♥

s ♦rt♠ ♥ p(θ|①) s ♠r♥ stt♦♥r② strt♦♥ strtsr♦♠ ♥ ♥t θ[0] t♥ tr♥ts t♥ t♦ st♣s t trt♦♥[r] t ♣r♦r♠s t t♦ ♦♦♥ st♣s

z[r] ∼ z|θ[r],x

θ[r+1] ∼ θ|z[r],x.

♦rt♠ s s♠♣r ♦r t ♠①tr ♠♦s

♠♣♥ ♦ t ss ♠♠rs♣ ♥♣♥♥ t♥ ♥s ♦st♦ s② s♠♣ t t♦r z s♥ p(z|θ[r],x) =

∏ni=1 p(zi|θ[r],xi) ♥ z

[r]i

s s♠♣ r♦♠ t ♦♦♥ ♠t♥♦♠ strt♦♥

z[r]i |θ[r],xi ∼ M(ti1(θ

[r]), . . . , tig(θ[r])).

♠♣♥ ♦ t ♣r♠trs ❲♥ tr s ♥♦ ♦♥str♥t t♥ t ♣r♠trs ♦ r♥t sss t ♦♦♥ ♦♠♣♦st♦♥ s s t♦ s♠♣ θ[r+1]

p(θ[r+1]|z[r],x) = p(π[r+1]|z[r])g∏

k=1

p(α[r+1]k |z[r],x).

♦t tt π s ♥♣♥♥t ♦ t t ♦♥t♦♥② ♦♥ t ss ♠♠rs♣s s ♣r♦r ♦ π s t ♦♥t r②s ♥♦♥ ♥♦r♠t ♣r♦r ♥ s s t♣r♦r ♥ t ♣♦str♦r strt♦♥s ♦ t ss ♣r♦♣♦rt♦♥s r rs♣t② ♥②

π ∼ Dg

(

1

2, . . . ,

1

2

)

♥ π|z[r] ∼ Dg

(

1

2+ ♥[r]

1 , . . . ,1

2+ ♥[r]

g

)

,

r r♠♥ tt ♥[r]k =

∑ni=1 z

[r]ik ❲ ♥♦ strt ts ♦rt♠ t t

r♥♥♥ ①♠♣

Page 49: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

❲ ss♠ tt t ♣r♦r ♦ σ2k s G−1(c0, C0) ♥ tt t ♣r♦r ♦ µk

♦♥t♦♥② ♦♥ σ2k s N1(b0, B

−10 σ2

k) trt♦♥ [r] ♦ t s s♠♣r♥ p(θ|x) s stt♦♥r② strt♦♥ s rtt♥ s ♦♦s

∀i = 1, . . . , n z[r]i |xi,θ[r] ∼ M2(ti1(θ

[r]), ti2(θ[r]))

π[r+1]|z[r] ∼ D2

(

1

2+ n

[r]1 ,

1

2+ n

[r]2

)

∀k = 1, 2 µ[r+1]k |x, z[r], σ2[r]

k ∼ N1(b[r]k , B

[r]k )

∀k = 1, 2 σ2[r+1]k |x, z[r], µ[r+1]

k ∼ G−1(c[r]k , C

[r]k ),

r b[r]k =B0b0+

∑ni=1 z

[r]ik xi

B0+n[r]k

B[r]k =

σ2[r]k

B0+n[r]k

c[r]k = c0 +n[r]k +1

2♥ C

[r]k =

C0 +12(∑n

i=1 z[r]ik (xi − µ

[r+1]k ) + B0(µ

[r+1]k − b0)

2)

♥♥♥ ①♠♣ s s♠♣r

♠♣ s♠♣♥ ♦♥t♦♥ s t s s♠♣r s t♦ ♣r♦r♠ ♥♠r♦ trt♦♥s t s s♦t② ♥ssr② tt st♣ ♥♦s s♠ s♠♣♥ t♠ t ♦♥t♦♥ strt♦♥s ♦ z[r]

i ♥ π[r+1] r ①♣t t s♠♣♥ ♦α[r+1] ♥ ♠♦r ♦♠♣t ♦♥t ♣r♦r strt♦♥s r s♦ ♥r②s s♥ t② ♣r♦ ss ♣♦str♦r strt♦♥ s t s♠♣♥ ♦ α[r+1]

s s② ♥ tr s ♥♦ ♦♥str♥t t♥ t ♣r♠trs ♥ t s r ts♠t♦♥ ♦ p(α[r+1]

k |z[r],x) s t♦♦ ♠ t♠ ♦♥s♠♥ ♥♦tr ♣♣r♦ t♥t s s♠♣r s t♦ s

tr♦♣♦sst♥s ♦rt♠

♥ ♠ ♦ t tr♦♣♦sst♥s ♦rt♠ s t♦ s♠♣ sq♥♦ θ ♦r♥ t♦ ts ♣♦str♦r strt♦♥ p(θ|x) s ♦rt♠ rqrs ♥ ♥str♠♥t strt♦♥ ♥♦t ② q(.;θ) ♥ t rs♣t t♦ t ♦♠♥t♥♠sr ♦ t ♠♦ t trt♦♥ [r] t ♥str♠♥t strt♦♥ ♥rts ♥t θ⋆ ♦♥t♦♥② ♦♥ t rr♥t ♦ θ ♥ t ♥t s ♣tt ♣r♦t② λ[r] ♥ ②

λ[r] = ♠♥

p(θ⋆|x)q(θ[r];θ⋆)

p(θ[r]|x)q(θ⋆;θ[r]); 1

.

Page 50: Model-based clustering for categorical and mixed data sets

Pr♠tr st♠t♦♥

s ♦rt♠ s p(θ|①) s stt♦♥r② strt♦♥ trt♥ r♦♠ ♥♥t θ[0] ts trt♦♥ [r] s rtt♥ s

θ⋆ ∼ q(θ;θ[r])

θ[r+1] =

θ⋆ t ♣r♦t② λ[r]

θ[r] t ♣r♦t② 1− λ[r].

♦rt♠ tr♦♣♦sst♥s ♦rt♠

②r

♥ ❲♥ st♣ ♦ s s♠♣r s t t♦ ♣r♦r♠ t ②r♠♠ ♦rt♠s r ♦t♥ s ♠♦st ♣♦♣r ♣♣r♦ s t♦ s♠♣ sq♥♦ θ ♦r♥ t♦ tr♦♣♦st♥s s♠♣r ♥ ts ♣♣r♦ t tst♣s ♦ t s s♠♣r r r♣ ② ♦♥ trt♦♥ ♦ tr♦♣♦sst♥s♦rt♠ ♦r t stt♦♥r② strt♦♥ ♦ t r♦ ♥ st②s q t♦p(θ, z|x)

s ♦rt♠ ♣r♦r♠♥ t ♥r♥ ♦r t ♠①tr ♠♦s s p(θ|①)s ♠r♥ stt♦♥r② strt♦♥ trt♥ r♦♠ ♥ ♥t θ[0] tstrt♦♥ [r] s rtt♥ s

z[r] ∼ z|θ[r],x

θ⋆ ∼ q(θ;θ[r])

θ[r+1] =

θ⋆ t ♣r♦t② λ[r]

θ[r] t ♣r♦t② 1− λ[r],

r q(.;θ) s t ♥str♠♥t strt♦♥ ♦ t tr♦♣♦sst♥s st♣♥ r λ[r] s ts ♣t♥ ♣r♦t② ♥ ②

λ[r] = ♠♥

p(θ⋆|z[r],x)q(θ[r];θ⋆)

p(θ[r]|z[r],x)q(θ⋆;θ[r]); 1

.

♦rt♠ tr♦♣♦st♥s s♠♣r

❲ ♥♦ strt ts ♦rt♠ t t r♥♥♥ ①♠♣

Page 51: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

trt♦♥ [r] ♦ ts ♦rt♠ s rtt♥ s

z[r] ∼ z|θ[r],x

π[r+1] ∼ π|z[r]

α⋆ ∼ q(α;α[r])

α[r+1] =

α⋆ t ♣r♦t② λ[r]

α[r] t ♣r♦t② 1− λ[r],

r q(.; .) s t ♥str♠♥t strt♦♥ ♦ t tr♦♣♦sst♥s st♣♥ r λ[r] s ts ♣t♥ ♣r♦t② ♥ ②

λ[r] = ♠♥

p(α⋆|z[r],x)q(α[r];α⋆)

p(α[r]|z[r],x)q(α⋆;α[r]); 1

.

♥♥♥ ①♠♣ tr♦♣♦st♥s s♠♣r

♦ st♦♥

♥ t ♠♦ st♦♥ ♥

♥t♦♥ ♦ t s ♦♥sr t ♥r ♥t ♠①tr ♠♦

p(xi;θ) =

g∑

k=1

πkp(xi;αk),

θ ∈ Θ r t ♣r♠tr s♣ Θ s ♥ ② t ♥♠r ♦ ♦♠♣♦♥♥ts ♥t ♥tr ♦ ♦♠♣♦♥♥t ♠♦ m r♦♣s t st ♦ t strt♦♥s♥ ② s♦

m = p(xi;θ) : θ ∈ Θ.

♠ ♠♦ m ♥s t ♥tr ♦ t ♦♠♣♦♥♥t strt♦♥s ♥ t♥♠r ♦ ♦♠♣♦♥♥ts s t s ♥r② ♥♥♦♥ t ♠♦ s t♦ ♥rr♦r♥ t♦ t t s ♥ ∆ s t st ♦ t ♠♦s ♦♥sr ②t ♣rtt♦♥r ♥ t ♠ s t♦ ♥ t st ♠♦ ♠♦♥ ∆

♦♦♦ ♥t♦♥ ♥ ♠ ♠♦s ♦♦ ♥t♦♥ ♥r② ♦s t♦ st♠t t st ♠♦ ♦r♥ t♦ t t ♦r ts♣♣r♦ ♥ ♥♦t rt② ♣♣ ♥ t ♠①tr ♠♦ ♦♥t①t ♥ ♥ s s ♦t ♦ ♠♦s r ♠ ♦r ♥st♥ ss♥ ♠①tr ♠♦ ttr ♦♠♣♦♥♥ts ②s ♦t♥s st ♦♦ s t♥ t ss♥ ♠♦st t♦ ♦♠♣♦♥♥ts s t st ♠♦ s t ♠♦ ♠s t sttr ♦ t♥ ts qt② ♦ st♠♥t t♦ t t ♥ ② ts ♦♦ ♥ ts ♦♠♣①t② ♥♠r ♦ ♣r♠trs

Page 52: Model-based clustering for categorical and mixed data sets

♦ st♦♥

♥♦r♠t♦♥ rtr ❲ s ♥ t♦♥ tt rst rtr ts♦♣ ♦ t ♦♦ ♥t♦♥ ♥ s t♦ st t ♥♠r ♦ sss s♣②♦r t ♦♠tr str♥ ♠t♦s ts ♣♣r♦s ♥ s t♦ st t♠♦ ♦ ♣r♦st ♠t♦ t s ♠♦r ♦♥♥♥t t♦ s ♥♦r♠t♦♥ rtr ♣r♦♣♦s ② t ♣r♦st r♠♦r s rtr rtr t r♦r♦s②♣r♦r♠ t ♠♦ st♦♥ ♦r♥ t♦ ♥ ♦t ♦ t st♠♥t rtr ♦r ♥ ♦t ♦ sst♦♥ rtr♦♥ ♥r② ts rtrrqr t ♠ rt t♦ ♠♦ ♥ ∆ s♥ t② ♥ ♦t♥ rtt♥ s ♣♥③t♦♥ ♦ t ♦♦♦ ♥t♦♥

m = L(θ;x)− h(νm),

r θ s t ♠ ♦ t ♠♦ m r νm s t ♣r♠trs ♥♠r ♦ t♠♦ m ♥ r h(.) s ♥t♦♥ ♥ ② t rtr♦♥ ♦t tt qt② ♦ ♥t ♦r t ♥♦r♠t♦♥ rtr♦♥ s t ♦♥sst♥② ♥ ♠♥s♦♥ssr♥ ♦♦ s②♠♣t♦t ♦r

♥t♦♥ ♦♥sst♥② ♥ ♠♥s♦♥ ♦r rtr♦♥ rtr♦♥ s ♦♥sst♥t♥ ♠♥s♦♥ t sts t s♠♣st tr ♠♦ t ♣r♦t② ♦♥ ♥ ts♠♣ s③ t♥s t♦ t ♥♥t②

♥♦r♠t♦♥ rtr ♦r t t st♠♥t

s st♦♥ s ♦t t♦ t ♠♦ st♦♥ st②s t ♣r♦♠ s❬❪ ♣tr ♦r t ♠①tr ♠♦s s♣② t st♦♥ ♦ t ss ♥♠r ♣r♥♣② s♥ t ♠♦s r ♠ ♥ s♥ t ♥♦r♠t♦♥ rtrr ♦♥② s②♠♣t♦t② tr

rq♥tst rtr♦♥

♥ rq♥tst r♠♦r t ♠ s t♦ ♥ t ♠♦ ♠♥♠③♥ t r r♥ ❬❪ ♦ t tr strt♦♥ rt t♦ t st♠t♦♥

♥t♦♥ r r♥ t xi ∈ Re t r

r♥ ♦ t ♣ f(xi) rt t♦ t ♣ g(xi) s

(f, g) =

xi∈X

f(xi) ln f(xi)dxi −∫

xi∈X

f(xi) ln g(xi)dxi.

t f(xi) t ♣ ♦ t tr ♠♦ ♥ t ♠♦ ♠♥♠③♥ s q♥tt♦ ♥ t ♠♦ ♠♥♠③♥ t tr♠ ♦♥ t♥ s ♦ t ♣r♦s qt♦♥s ♦r ♠♦ m t ♠ s t♦ ♦♠♣t

η(xi; f,m, θ) =

xi∈X

f(xi) ln p(xi; θ)dxi,

r p(xi; θ) s t ♣ ♦ t ♠♦ m ♣r♠tr③ ♥ ts ♠ θ s t strt♦♥ f s ♥♥♦♥ s t ♥tr st♠t♦r ♦ η(xi; f,m, θ) ♥ ②

η(xi; f ,m, θ) =1

nL(θ;xi).

Page 53: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

♦r ts st♠t♦r srs r♦♠ t ♦♦♥ s

b = Ef

[

η(xi; f ,m, θ)− η(xi; f,m, θ)]

.

s t st ♠♦ ♠♦♥ ∆ ♠①♠③s t ♦rrt ♦♦♦

argmaxm∈∆

L(θ;x)− b.

❬❪ s♦ tt t ♦rrt tr♠ s s②♠♣t♦t② q t♦ t ♥♠r♦ ♣r♠trs

♥t♦♥ rtr♦♥ ♥♦r♠t♦♥ rtr♦♥ s♥ s

(m) = L(θ;x)− ν,

r θ s t ♠ ♦ t ♠♦ m ♥ ν ts ♥♠r ♦ ♣r♠trs

s t rtr♦♥ s ♥ st♠t♦r ♦ t ①♣tt♦♥ ♦ t ♠♥ ♦ t ♦♦♦ st② ♦ t rtr♦♥ ♣r♦♣rts ♥ ♦ ts ①t♥s♦♥ s ♥ ❬♦③❪ ♦r t ♦r ♦ t rtr♦♥ ♥ ♥♦♥sst♥t

Pr♦♣♦st♦♥ s ♥♦t ♦♥sst♥t s ♥♦t ♦♥sst♥t ♥ ♠♥s♦♥ ♥♠♦s t t s♠ ♥♠r ♦ ♦♠♣♦♥♥ts r ♠

Pr♦♦ t ♠♦ m0 ♦s t ♠ ♦ ♠♥s♦♥ ν0 s ♥♦t ② θ0 ♥ t m1

♦s t ♠ ♦ ♠♥s♦♥ ν1 s ♥♦t ② θ1 s s m0 s t tr ♥♠r m0

♥ m1 r ♠ ♠♦s t t s♠ ♥♠r ♦ ♦♠♣♦♥♥ts ♥ ν0 < ν1♥

2 ((m1)− (m0)) = 2(

L(x; θ1)− L(x; θ0))

− 2(ν1 − ν0)

D→ χ2ν1−ν0

− 2(ν1 − ν0).

s t rtr♦♥ s ♥♦t ♦♥sst♥t limn→∞

P ((m1) > (m0)) > 0)

s♥ P (χ2ν1−ν0

> 2(ν1 − ν0)) > 0 ♦t tt t ♠♦♥strt♦♥ ♥ ♥♦t ♣r♦r♠ t♦ st t ♥♠r ♦ sss ♥ ♥ s s t ♦♥r♥ t♦ t♦♦ rt♦ s ♥♥♦♥ s♥ t ♥♦s ②♦rs ♦♣♠♥t ♦♥ t ♦rr♦ t ♣r♠trs s♣ ♦r t s ♦t♥ ♥ ♦sr tt t rtr♦♥sts ♠♦r ♦♠♣t ♠♦s ♥ t tr ♦♥ s ♥ t st ♦ t ♠♦s❬❪

②s♥ rtr♦♥

t p(m) t ♣r♦r strt♦♥ ♦ m ∈ ∆ t ♣♦str♦r strt♦♥ ♦ ♥trsts ♥ ② s♥ t ②s r s ♦♦s

p(m|x) = p(x|m)p(m)

p(x).

Page 54: Model-based clustering for categorical and mixed data sets

♦ st♦♥

♥ ♥ ②s♥ ♣♦♥t ♦ t st ♠♦ m⋆ ♠①♠③s t ♣♦str♦rstrt♦♥

m⋆ = argmaxm∈∆

p(m|x) = argmaxm∈∆

p(x|m)p(m).

s t q♥tt② ♣r♦r♠♥ t ♠♦ st♦♥ s t ♥trt ♦♦ s♦♥♠ ♠r♥ ♦♦ ♦r ♥ ♥ ②

p(x|m) =

θ∈Θ

p(x,θ|m)p(θ|m)dθ,

r t ♣r♠tr s♣ Θ ♣♥s ♦♥ m ts q♥tt② ♥ ♣♣r♦ ♠♥② ♠t♦s s t r ♦ ❬❲❪ t ♠♦st ss ♦♥ s t♦ st ♣♣r♦①♠t♦♥ ❬❪ ♣♣r♦①♠ts ln p(x|m) ② s♥ ♣♣♣r♦①♠t♦♥ ♥ ② r♣♥ t ♠♣ ② t ♠

♥t♦♥ rtr♦♥ ②s♥ ♥♦r♠t♦♥ rtr♦♥ s♥ s

(m) = L(θ;x)− ν

2lnn,

r θ s t ♠ ♦ t ♠♦ m ♥ r ν ♥♦ts ts ♥♠r ♦ ♣r♠trs

s rtr♦♥ ss♠s rrt② ♦♥t♦♥s ♦♥ t ♣ ♠② ♥♦t r② t ♠①tr ♠♦s rtr♠♦r ts ♣♣r♦①♠t♦♥ s ♦♥② s②♠♣t♦t② tr

Pr♦♣♦st♦♥ s ♦♥sst♥t rtr♦♥ s ♦♥sst♥t ♥ ♠♥s♦♥♥ ♠♦s t t s♠ ♥♠r ♦ ♦♠♣♦♥♥ts r ♠

Pr♦♦ t ♠♦ m0 ♦s t ♠ ♦ ♠♥s♦♥ ν0 s ♥♦t ② θ0 ♥ t m1

♦s t ♠ ♦ ♠♥s♦♥ ν1 s ♥♦t ② θ1 s s m0 s t tr ♥♠r m0

♥ m1 r ♠ ♠♦s t t s♠ ♥♠r ♦ ♦♠♣♦♥♥ts ♥ ν0 < ν1♥

2 ((m1)− (m0)) = 2(

L(x; θ1)− L(x; θ0))

− 2(ν1 − ν0) lnn

D→ χ2ν1−ν0

− 2(ν1 − ν0) lnn.

② s♥ t ♥♦tt♦♥ ∆ν = ν1− ν0 t ♦♦♥ rst s ♦t♥ ② ♣♣②♥ t②s ♥qt② ♥ n s r

P ((m1) > (m0)) ≤ P (|χ2∆ν

−∆ν | > ∆ν(−1 + lnn))

≤ 2∆ν

(∆ν(−1 + lnn))2n→∞→ 0,

s♥ E[χ2∆ν

] = ∆ν ♥ ❱r(χ2∆ν

) = 2∆ν s t rtr♦♥ s ♦♥sst♥t♥ ♠♥s♦♥ ♥ ♠♦s t t s♠ ♥♠r ♦ ♦♠♣♦♥♥ts r ♠♦t tt t ♠♦♥strt♦♥ ♥ ♥♦t ♣r♦r♠ t♦ st t ♥♠r ♦ sss♥ ♥ s s t ♦♥r♥ t♦ t ♦♦ rt♦ s ♥♥♦♥ s♥ t♥♦s ②♦rs ♦♣♠♥t ♦♥ t ♦rr ♦ t ♣r♠trs s♣ ♦r t rtr♦♥ s ♠♦r r♦st t♥ t ♦♥ t ♥ ♦rst♠ts t ♥♠r♦ ♦♠♣♦♥♥ts ♥ t tr ♠♦ s ♥♦t ♥ ∆ ❬❪

♦t tt ② s♥ ♦② ♦♥ ♣r♠tr③t♦♥ ❬r❪ s♦s tt t rtr♦♥ s ♦♥sst♥t st♠t♦r ♦ t ♦rrt ♥♠r ♦ ♦♠♣♦♥♥ts ♥ t strt♦♥

Page 55: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

rs ♠♣ ∆ s r ♦r t st♠t♦♥ ♦ t ♣r♠trs s ♦♠♣①t♥ t ①st ♣♣r♦ s ♥♦t ♦ ♥ ts ♣♣r♦ ♦♥ssts ♥ ♦♠♣tt♦♥ ♦ ♥ ♥♦r♠t♦♥ rtr♦♥ ♦r t ♠♦s s♦ t s t♠ ♦♥s♠♥rtr♠♦r t ♣rtt♦♥r ♦♥② ss t st♠t ss♦t t♦ t st ♠♦s t ♦tr st♠ts r ♥♦t s ♦r t t ♥②ss s r s♦ ② t ♣♣r♦ ♦ t rrs ♠♣ ❬r ❪ r t ♠♦ ♥t ♣r♠trs r s♠t♥♦s② st♠t ❯♥♦rt♥t② ts ♣♣r♦ ♥♦st ♦♠♣tt♦♥ ♦ t ♣r♦ts ♦ t ♠♦ tr♥st♦♥ ♥ ♦♠♣①♦r ts ♦t ♦♥ssts ♥ ♦♥ t st♠t♦♥ ♦ t ♣r♠trs ♦ t ♠♦s ♥ ∆ t ♥ ♦♠ ♠♥t♦r② ♥ t ♠♦ s♣ ♦♠s s Prt

♥♦r♠t♦♥ rtr♦♥ ♦r t ♣rtt♦♥ st♠♥t

♥ Pr♦①② t ♦♥sst♥② ♦ t ♥♦r♠t♦♥ rtr♦♥ ♥ r ♥ s t ♠♦s r r♦♥ t rtr♦♥ s②♠♣t♦t②♦rst♠ts t ♥♠r ♦ ♦♠♣♦♥♥ts ♦r♥ t♦ t ss s♣rt♦♥ ♦ r♥ ① ♥ ♦rt ❬❪ ♣r♦♣♦s t♦ ♥ sst♦♥♦t ♥ t ♥♦r♠t♦♥ rtr♦♥ ♥ s s t st ♠♦ ♠①♠③s t♥trt ♦♠♣tt ♦♦ s t t♦r z s ♥♥♦♥ t s r♣ ②ts ♠♣ ♥♦t ② z t t t ♠

♥t♦♥ ①t ♥trt ♦♠♣tt ♦♦ sss ♠♦ t sst♦♥ ♠ t s ♥ s

①(m) = ln p(x, z|m)

= ln

θ∈Θ

p(x, z|m,θ)p(θ|m)dθ.

♦r ♥ t ♥tr ♥ ①♣t s ♦r ♥st♥ t st② ♥❬❪ ♦r t ♠ ♦ ♠t♥♦♠ strt♦♥s t s ♥♦t ♥r② t ss ♥ ♣♣r♦①♠t rs♦♥ ♦ ts rtr♦♥ s

♥t♦♥ ♥trt ♦♠♣tt ♦♦ ♥ ♣♣r♦①♠t ②

(m) = ln p(x, z|m, θ)− ν

2lnn.

Page 56: Model-based clustering for categorical and mixed data sets

♦♥s♦♥

♣♣t♦♥ ♦♥ r t st ♦ t ♥♦r♠t♦♥ rtr

❲ ♥ ①♠♣ ♦ t s♥ ♦ t ♥♦r♠t♦♥ rtr ♦♥ t t t st ♦r r♥t ♥♠rs ♦ sss t ♦rt♥ ♣rs♠♦♥♦sss♥ ♠①tr ♠♦s ❬❪ ♦♠♣♦s t st ♦ t ♦♥sr ♠♦s r s♣②s t s ♦ t ♥♦r♠t♦♥ rtr ♦r r♥t♥♠rs ♦ sss ♦♦♦ ♥t♦♥ s ♥rs♥ t t ss ♥♠r rtr♦♥ sts ♦r sss t rtr♦♥ sts tr sss♦r ♦t rtr stt t♦ st t ♥♠r ♦ sss s t♣rtt♦♥r s♥ rs♣t② ♥ s♦ ♥②s t ♣rtt♦♥ ♥tr sss rs♣t② t♦ sss ♥② t rtr♦♥ str♦♥②sts t ♣rtt♦♥ ♥ t♦ sss s ♣rtt♦♥ s♠s rst ♦r♥t♦ t sttr ♣♦t

t t st ♦ st♦♥ ② ♥♦r♠t♦♥ rtr

1 2 3 4 5 6

−12

50−

1200

−11

50

number of classes

crite

rion

valu

es

log−likeaicbicicl

♥♦r♠t♦♥ rtr

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

5060

7080

90

eruptions

wai

ting

ttr ♣♦t t t ♣rtt♦♥ ♥ t♦ sssst ②

r rtr♦♥ s ♦r t st ♦ t ♦rt♥ ss♥ ♠①tr ♠♦s♦r r♥t ♥♠r ♦ sss t♦ str t t t st

♦♥s♦♥

s ♦r♣ ♣tr s ♣rs♥t t ♥r r♠♦r ♦ t ♠①tr ♠♦s ♥ s ♥ strt ② t ♦♠♣♦♥♥t ss♥ ♠①tr ♠♦s t s ♣♦♥t♦t tt t ♥r ♠ ♠♦ ♦s t♦ s② str ♦♠♣① t t♦r ♦r ♠① t t t rs ♦ s ♥ t t r♥trss ♦rrt

♦t st♠t♦♥ ♠t♦s rq♥tst ♥ ②s♥ ♥ ♣rs♥t ♥ tstss ♦r t rq♥tst ♣♣r♦ t♦ ♥r t ♣r♠trs s♥ t ♦s ♥♦t

Page 57: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss stt ♦ t rt

rqr s♦♠ ♥♦r♠t♦♥ ♣r♦r ♦r t ♠ s ♥trt ♣r♦r♠t ♥r♥ ♥ t ②s♥ r♠♦r ♥ s s ♦r t ♦♥t ♣r♦rstrt♦♥s ♥ ♦rr t♦ s② st♠t t ♣r♠trs ②♣r♣r♠trs ♦t ♣r♦r strt♦♥s r st t♦ ② ♥♦r♠t

♠♦ st♦♥ s ♣r♦r♠ ② s♥ t rtr♦♥ s t ♠♦st ♦♠♠♦♥♥♦r♠t♦♥ rtr♦♥ ♦r t ♠①tr ♠♦s

♣r♣♦s ♦ t ♦♦♥ ♣trs s t♦ st② ♥ t♦ ♣r♦♣♦s ♥ ♠①tr♠♦s ♦♥ t♦ str ♦♠♣① t t♦t ss♠♥ t ♦♥t♦♥ ♥♣♥♥ t♥ t rs ❲ ♥♦ ♦s ♦♥ t t♦r t st str♥

Page 58: Model-based clustering for categorical and mixed data sets

Prt

♦s str♥ ♦r

t♦r t

Page 59: Model-based clustering for categorical and mixed data sets
Page 60: Model-based clustering for categorical and mixed data sets

s ♣rt ♦t t♦ t str ♥②ss ♦ t♦rt s s♣t ♥t♦ ♦r ♣trs rst ♦♥ ♣rs♥ts ♥ ♦r ♦ t str♥ ♣♣r♦s ♦t t♦ t t♦r t sts ❲ ♠♥②♦s ♦♥ tr ♠♥ ♠♦s ♣♣r♦s t ♦♥r ♠①tr ♠♦s t ♠①trs ♦ trs ♥ t ♠t t♥t ss ♠♦s s ♠♦s r strt♦♥ s♠ r t st s♦♥ ♥ t tr ♣trs ♣rs♥t ♦r ♦♥trt♦♥s t♦ ts r♠♦r ❲ ♣rs♥t t♦ ♥ ♠①tr♠♦s r ①t♥s♦♥s ♦ t ss t♥t ss♠♦ ♦r s ♠♦s t rs r r♦♣ ♥t♦♦♥t♦♥② ♥♣♥♥t ♦s s♣ strt♦♥s ♦ t ♦s ♠♦③ t ♥trss ♣♥♥ss rsts r ♣rt ♦ t♦ s♠tt rts st ♣tr s ♦t t♦ ♣r♦♣♦s ♠♦ ♦♠♣rs♦♥ strt ♦♥ t ①♠♣ ♦ t ♦r ♣tr s♦♥ ♣r♣♦s ♦ ts ♣tr s t♦ ♣rs♥t ♦r ♣s ♣r♦r♠♥ t ♥r♥ ♦ ♦t ♣r♦♣♦s♠♦s

♦s ♦♥② sr♠♥ ♦ tt t s t t t t trr♦s ♥ tr ♣s r t♥ ②♦trs ♦ r ♦♥♥ tt t

s t t ♦ t♦♥ t♥ ♦rt t

Page 61: Model-based clustering for categorical and mixed data sets
Page 62: Model-based clustering for categorical and mixed data sets

♦ ♦♥t♥ts

str ♥②ss ♦ t♦r t sts stt ♦ t rt

♥ ♦ str ♥②ss ♦r t♦r t

♦♠tr ♣♣r♦s

♦♥r ♠①tr ♠♦s

①trs ♦ trs

t t♥t ss ♠♦

♦♥s♦♥

♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s

♥tr♦t♦♥

①tr ♦ ♥trss ♥♣♥♥t ♦s

Prs♠♦♥♦s ♦ strt♦♥

①♠♠ ♦♦ st♠t♦♥ ♦rt♠

♦ st♦♥ ♦rt♠

♠r ①♣r♠♥ts ♦♥ s♠t t sts

♥②ss ♦ t♦ r t sts

♦♥s♦♥

♦s str♥ t ♦♥t♦♥ ♣♥♥② ♠♦s

♥tr♦t♦♥

①tr ♠♦ ♦ ♠t♥♦♠ strt♦♥s ♣r ♠♦s

①♠♠ ♦♦ st♠t♦♥ ♥ ♦rt♠

♦ st♦♥ tr♦♣♦st♥s s♠♣r

♠r ①♣r♠♥ts ♦♥ s♠t t sts

♥②ss ♦ t♦ r t sts

♦♥s♦♥

♦ ♦♠♣rs♦♥ ♣r♦r♠ ② tr ♣s

strt

♦♦s

Page 63: Model-based clustering for categorical and mixed data sets

♦ ♦♥t♥ts

♦♥s♦♥ ♦ Prt

Page 64: Model-based clustering for categorical and mixed data sets

♣tr

str ♥②ss ♦ t♦r t

sts stt ♦ t rt

♣r♣♦s ♦ ts ♣tr s t♦ ♣rs♥t t ♠♥ ♣♣r♦s t♦ str t♦r t sts rst st♦♥ s ♦t t♦ t ♦♠tr ♠t♦s♥ ♠♦r ♣rs② t♦ t ♠♥s ♦♥s ♦tr st♦♥s sr t tr ♠♥ ♠♦s♠t♦s ♦r ♣rs② t s♦♥ st♦♥ ♦ss♦♥ t ♦♥r ♠①tr ♠♦s t s♦ ts tss t♥t ss ♠♦ s s♣ ♠♦♦ t ♦♥r ♠①tr ♦♥ ss♠♥ t ♦♥t♦♥♥♣♥♥ t♥ t rs s ♠♦ s ♦♥trst ♦r s s♥ t ♣r♦♣♦s ♠♦s ♥tr♦ ♥t t♦ ♦♦♥ ♣trs ♦♥sst ♥ t♦ ①t♥s♦♥s♦ ts ♠♦ tr st♦♥ s ♦t t♦ t ♣rs♥tt♦♥ ♦ t tr ♠①tr ♠♦s st st♦♥♣rs♥ts t ♠t t♥t ss ♠♦ss ♠♦s r strt ♦♥ r t st tr♦♦t ts ♣tr

②s ♦ s♦r t ②♦ s ②♦♦ r♥ t t ②♦ t♦ ♣

②♦r ♠♦t str♥st ♠♥② ♦rt♣♣② ♦ r♥s ♦♠r

♥ ♦ str ♥②ss ♦r t♦r t

♥tr♦t♦♥ t♦r rs r ♦t♥ ♣rs♥t ♥ t t sts s♥t② r s② ss t② ♥♦ ② s rs s ♦ rst②t s ♥♦t ♦♥♥♥t t♦ s③ t♦r t ♥ tr ♥t s♣ ♦ ts s t♦ ♦♥tr♥ ② ♥ s② ♥tr♣rtt♦♥ ♦ t ♣rtt♦♥ ♦♥② t

Page 65: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss ♦ t♦r t sts stt ♦ t rt

♦♠♥t♦r ♣r♦♠s r qt♦s ♥ s♦♠ ♥trss ♣♥♥s t♦ ♠♦③ ♥♠r ♦ ♣r♠trs ♥ ♥♠r ♦ ♠♦s ♥ ♦♠♣tt♦♥ s t ♣♣rs ♠♣♦rt♥t ♦r s tt t ♠♦s s t♦ str rs♣t tt♦ ♦♦♥ ♦ts

♦ r ♦ts s ♦♥ t ♠♦s ♣rs♥t ♥ ts ♦r♣♣tr ♣t t t ♦♥ t ♦♦♥ r ♦ts

♦s t♦ ♣r♦ ♠♥♥ ♣r♠trs t♦ ♦♥tr♥ t ♦ s③t♦♥

♦s t♦ t ♥t♦ ♦♥t t ♥trss ♣♥♥s ② ♠t♥ t♦♠♥t♦r ♣r♦♠s rt t♦ ♦t ♦ t ♥♠r ♦ ♣r♠trs ♥ ♦t ♠♦ st♦♥

t r♦♦t ts ♣rt ♦♥sr t drt t♦r ♦ t♦rrs ♥♦t ② xi = (x1

i , . . . ,xdi ) ♥ ♥ ♥ s♣ X t♦r

r xji = (xjhi ;h = 1, . . . ,mj) smj ♠♦ts ♥ ss ♦♠♣t s♥t♦♥ s s xjhi = 1 ♥ i ts ♠♦t② h ♦r r j ♥ xjhi = 0♦trs

trtr ♦ ts ♣tr t♦♥ ♦ss ♦♥ t ♦♠tr ♠t♦s ♣r♠tt♥ t♦ str t♦r t sts ♦tr st♦♥s r ♦t t♦ ♣r♦st♠t♦s ♥ t♦♥ ♣rs♥ts t ♦♥r ♠①tr ♠♦ s t rr♥ t♦ str t♦r t ♥ ♣r♦st r♠♦r t♦♥ ♣rs♥tst ♠①tr ♦ ♣♥♥② trs t♦♥ ♣rs♥ts t ♠t t♥t♠♦s

♥♥♥ ①♠♣ r♥ ts ♣tr t r♥t ♠t♦s ♦♥ t♦ strt♦r t sts r strt ♦♥ t ♥♠♥s ♥tstr② t ❬❪ s ss t♦r t st

s ♥r② t st ♣rs♥t ♥ ♦♥ssts ♥ t ♥♦ss ♥② ♥tsts ♦r ♣r♠♦rs ♥ ♠♦rs ♠ s t♦ rtr③t ♦r ♦ t ♥tsts ♦r♥ t♦ tr ♥♦ss

♥♥♥ ①♠♣ ♥tstr② t st

♦♠tr ♣♣r♦s

t♦s ♦♥ t ♥t s♣

♠♥s ♦rt♠ ♦r t♦r t

♥ ♠♥s ♦rt♠ ♦r t♦r t ♣r♦♣♦s ② ♠♦♥r♥② ❬❪ ss t ♦♠♣t s♥t ♦♥ ♦ t t♦r rs

Page 66: Model-based clustering for categorical and mixed data sets

♦♠tr ♣♣r♦s

♥tst ♥tst rq♥② rq♥②

♦r♣ r♦ss♥♦ss ♦ ♠♦rs ♥ ♣r♠♦rs ② ♥tsts ❬❪ t r ♥♦s s s♦♥ ♦r r♦s

♥ ♦r s ♣♣r♦ xjhi s ♦♥sr s ♥r② r st♥s t♦ str s t sqr ♦♥ tt ♦ t

sqr st♥ sqr st♥ ts ♥t♦ ♦♥t t t ♦ ♠♦t② ♥ t st♥ ♦♠♣t ② ♥ ♠♦r ♠♣♦rt♥ t♦ t rr♠♦ts t♥ t♦ t ♠♦st ♦♠♠♦♥ ♦♥s

♥t♦♥ sqr st♥ t ① = (x1, . . . ,xn) t♦ t s♠♣ ♦♠♣♦s ② n ♥s xi sr ② d t♦r rs sqr st♥ t♥ xi1 ♥ xi2 t 1 ≤ i1, i2 ≤ n s ♥ ②

Dχ2(xi1 ;xi2) =d∑

j=1

mj∑

h=1

(xjhi1 − xjhi2 )2

njh,

r njh =∑n

i=1 xjhi

♦♠♠♥ts ♠♥ r ♦ ts ♣♣r♦ s tt t t♦r ♦ t ss♠♥sr s t♥ ③r♦ ♥ ♦♥♦s ♥♦t ♥t t rtrsts ♦t sss ♦t♥ ♣rtt♦♥ s s♦ ② ♥tr♣rt

♠♦s ♦rt♠ ♦r t♦r t

♥ ♠♦s ♦rt♠ s ♥ ♣r♦♣♦s ② ❩ ♥ ❬❪ t♦♦ t ♣r♦♠ rt t♦ t str ♥②ss ♦ r t♦r t sts s

Page 67: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss ♦ t♦r t sts stt ♦ t rt

♦rt♠ ①t♥s t ♠♥s ♦♥ ② s♥ s♠♣ ♠t♥ ss♠rt② ♠sr♦r t♦r rs t trt♦♥ t ♣ts t ♠♦s ② rq♥②s ♠t♦ ♥ ♦rr t♦ ♠♥♠③ ♦st ♥t♦♥

ss♠rt② ♥ ♠♦s

♥t♦♥ t♥ ss♠rt② t t♦ drt t♦r rs xi1♥ xi2 ♠t♥ ss♠rt② ♦♥ts t ♠s♠ts t♥ ♦t xi1 ♥xi2 s ss♠rt② s ♥ ②

D1(xi1 ,xi2) =d∑

j=1

δ(xji1 ,xji2) t δ(xji1 ,x

ji2) =

1 xji1 6= xji2

0 xji1 = xji2.

♥t♦♥ ♦ ❬❪ ♠♦ ♦ t s♠♣ ① = (x1, . . . ,xn) s t♦rµ ∈ X s s µ = (µjh; j = 1, . . . , d;h = 1, . . . ,mj) ♠♥♠③s

D(①;µ) =n∑

i=1

D1(xi;µ).

♦t tt µ s ♥♦t ♥ssr② ♥ ♠♥t ♦ ① ♥ tt t s ♥♦t ♥ssr② ♥q

♣t♠③ rtr♦♥ ❲♥ t ss♠rt② ♥ ② ♥t♦♥ s st♥ t ♠♦s ♦rt♠ ♦♣t♠③s t ♦♦♥ rtr♦♥

I(③,θ;①) =n∑

i=1

g∑

k=1

d∑

j=1

δ(xji ,µjk),

r θ = (µ1, . . . ,µg) ♥ r µk s t ♠♦ ♦ ss k

♦rt♠

trt♥ r♦♠ ♥ ♥t θ[0] ts trt♦♥ [r] s rtt♥ ss ♠♠rs♣ z

[r] = argminz

I(z,θ[r];x)

z[r]ik =

1 k = argmink′

D1(xi,µ[r]k′ )

0 ♦trs

♥tr♦ st♠t♦♥ θ[r+1] = argminθ

I(z[r],θ;x)

µ[r+1]k = argmin

µk

n∑

i=1

z[r]ikD1(xi;µk).

♦rt♠ ♠♦s ♦rt♠

Page 68: Model-based clustering for categorical and mixed data sets

♦♠tr ♣♣r♦s

♦♠♠♥ts ♠♦s ♦rt♠ t ♠♥s ♦♥ ♦♥rs t♦ ♦♠♥♠♠ ♦ t ♥t♦♥ I(③,θ;①) t s s♦ ♠♥t♦r② t♦ ♣r♦r♠ r♥t ♥t③t♦♥s ♥ ♦rr t♦ ♦♣ t♦ t t ♦ ♠♥♠♠ ♦ ts ♥t♦♥ ♥② ♥♦ttt t ♥tr♦ st♠t♦♥ st♣ s tt ② t ♥t♦♥ ♦ D1(., .) ♥ts ♦♣t♠③t♦♥ s ♣r♦r♠ ♦♦r♥ts ② ♦♦r♥ts

♣♣r♦ s ♦♥ t rtr♦♥ s ♦♠♣t ♦r r♥t ♥♠rs♦ sss t♦ ♣rtt♦♥s ♦ ♦ ♥trst

♥tr♣rtt♦♥ rst ♦♥ s♣ts t t ♥t♦ t♦ sss ♦s t♠♦s r ♥ ② ♦t ♠♦st ♣rs♥t ♥♦ss ♥tsts ♠ ttt t♦♦t s s♦♥ ♥ ♥tsts ♠ tt t t♦♦t s s♦♥ ①♣t tst ♥tst s♦♥ ♦♥ s♣ts t t ♥t♦ tr sss t st t t♦ ♣r♦s ♠♦s t ♥♦ss r ♥tsts ♠ tt tt♦♦t s r♦s ①♣t t rst ♥tst s② t ♠♦s ♣♣r♦♦s ♥♦t ♦ t♦ r② ♥rst♥ ts t st

♥♥♥ ①♠♣ ♠♦s str♥

t♦s ♦♥ t t♦r s♣

♥ ❲♥ ♠♥② rs r ♦rrt t② ♣r♦ s♦♠ r♥♥t ♥♦r♠t♦♥ s t ♥ ♥t t♦ ♣r♦r♠ st♦♥ ♦ rs ♦r rt♦♥♦ t s♣ ♠♥s♦♥ ❲♥ t rs r t♦r t♣ ♦rrs♣♦♥♥ ♥②ss ♥ s ♥ ♦rr t♦ r t s♣ ♠♥s♦♥ ♥ts ♠t♦ ♣r♦s ♥♠r ♦♦r♥ts ♦r ♥ r♦r t s♣♦ss t♦ s ss ♦♠tr ♣♣r♦ t♦ str ♥♠r t t ♠♥s ♦rt♠ ❲ ♣rs♥t t ♠t♦ ♦ ♥ ❲ ♦♥ ♥ ❨ ♥❬❪ ♦♠♥s ♥ ♠♥s ♦rt♠ ♥ ♥ r♠♦r

♦tt♦♥s ❲ r♠♥ tt t s♠♣ ① = (xi; i = 1, . . . , n) s ♦♠♣♦s t♥s sr ② d t♦r rs s s♥t ♦♥ tf ♥♦t♥ t ♠tr① ♦ s③ n× d r d ≤ mj ♦rrs♣♦♥s t♦ t d♠♥s♦♥r♣rs♥tt♦♥ ♦ t d t♦r rs t wj t ♠tr① ♦ ts ♦ s③mj × d ❲ ♦♥sr ③ s t ♠tr① ♦ s③ n× g r t r♦s ♦rrs♣♦♥ t♦ t♥s ♥ t ♦♠♥ t♦ t ss ❲ ♥♦t ② θ t ♠tr① ♦ t ♥tr♦s ♦ t str ♥ t t♦r s♣

♣t♠③ rtr♦♥ ♠ s t♦ ♦♠♥ ♥ ♠♥s ♦rt♠ s♦t ♣r♦♠ s q♥t t♦ t ♠♥♠③t♦♥ ♦ t ♦♦♥ rtr♦♥

Iα1,α2(③,θ;①) = α1

d∑

j=1

SS(f − ①jwj) + α2SS(f − ③θ),

Page 69: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss ♦ t♦r t sts stt ♦ t rt

r α1 > 0 α2 > 0 α1 + α2 = 1 r ①j s t ♠tr① r t ♠♥t (i, h)s q t♦ ♦♥ ♥ i ts ♠♦t② h ♦r r j ♥ s q t♦ ③r♦♦trs ♥ r SS(f) = tr(f ′f)

♠r ♥ t ♦♣ α1, α2) ❲♥ α1 = 1 t rtr♦♥ ♥ ② st st♥r ♦♥ ♦r ❲♥ α2 = 1 t rtr♦♥ ♥ ② s q♥tt♦ t st♥r ♦♥ ♦r t ♠♥s ♦rt♠ s ♦r ♦trs s ♦ (α1, α2)ts rtr♦♥ ♣r♦r♠s tr♦ t♥ t ♥ t ♠♥s ♦ts

♦rt♠ st♠t♦♥ ♦ (f ,wj, ③,θ) s ♣r♦r♠ ② t tr♥t♥st sqrs ♦rt♠ ♣r♦♣♦s ② ❬❪ s ♦rt♠ ♦♥rs t♦ ♦ ♠♥♠♠ ♦ t ♥t♦♥ Iα1,α2(③,θ;①) ♦ sr r♥t ♥t③t♦♥s ♦ts ♦rt♠ t♦ ♦♥ ♥ ♦rr t♦ ♦t♥ t st♠t♦rs ♠♥♠③♥ tsrtr♦♥

trt♥ r♦♠ ♥ ♥t θ[0] ts trt♦♥ [r] s rtt♥ ❲t ♠tr① ♥ ♥tr♦s ♦♣t♠③t♦♥

wj[r] = (①j′①j)−1①j

′f ♥ θ[r] = (③[r]

′③[r])−1③[r]

′f .

t♦r s♣ ♦♣t♠③t♦♥

f [r+1] = argmaxf

tr

(

f ′

[

α1

d∑

j=1

①j(①j′①j)−1①j

′+ α2③

[r](③[r]′③[r])−1③[r]

]

f

)

.

Prtt♦♥ ♦♣t♠③t♦♥

③[r+1] = argmin③

SS(f [r+1] − ③θ[r]).

♦rt♠ ♦rt♠ ♠♥♠③♥ Iα1,α2(③,θ;①) ❬❪

♦♠♠♥ts ♥ t♦♥ t♦ t ss ♠ts ♦ t ♦♠tr ♣♣r♦s tr♣r♦♠s r rs ② ts ♠t♦

rst ♣r♦♠ s ♦t t ♣r♠trs (α1, α2) r ① ② tsr ♥ tr s ♥♦ r ♥t② tr♠♥s t♠ tr♠♣ts ♦♥ t ♣rtt♦♥ r s♥♥t

s♦♥ ♣r♦♠ s ♦t t s③ ♦ t t♦r s♣ ♥ t s♣♠♥s♦♥ d s rtrr② ① ② t sr t rs ♦ ♦♦s♥ ♥♦r♠t♦♥

st ♣r♦♠ s ♦t t ss ♥tr♣rtt♦♥ s ♦♠♣① ♥t sss r s♠♠r③ ② t ♥tr♦s r ♥♦t ♥ ♥ t♥t s♣ t ♥ t t♦r s♣ rt ② ♦♠♥t♦♥s ♦ t ♦r♥rs

Page 70: Model-based clustering for categorical and mixed data sets

♦♥r ♠①tr ♠♦s

♦♥r ♠①tr ♠♦s

♥ ❲ s♥ ♥ ♣tr tt t ss♥ ♠♦ ♥ ss ♦♠♣♦♥♥t strt♦♥ ♥ t rs r ♥♠r ♥ t s♠ ② t♦♥r ♠♦ s t♦r t ♥②ss ② rst ❬r❪ s ♥tr②s s ♦♠♣♦♥♥t strt♦♥ ♥ t rs r t♦r ♦r t♦♠♣t ♦♥r ♠♦ st♠ts t ♣r♦t② ♦ t ♠♦t② r♦ss♥s ts s♦ ♠♥t♦r② t♦ ♠♣♦s ♦♥str♥ts ♦♥ ts ♠♦ t♦ t ♦♥r ♠①tr♠♦

trtr ♦ ts st♦♥ ss ♣♣r♦ ss♠s ♦♥t♦♥ ♥♣♥♥ t♥ t rs s ♠♦ s ♥♠ t♥t ss ♠♦ ♦r ♥ ②s❬♦♦❪ ♥ s ♥ t ♥ t♦♥ tr ♦♥r ♠①tr ♠♦s r① t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ r ♣rs♥t ♥ t♦♥

t♥t ss ♠♦

♦ ♣rs♥tt♦♥

♥ s ♠①tr ♠♦ ss♠s tt t rs r ♥♣♥♥t ♦♥t♦♥② ♦♥ ss ts ts ♦♠♣♦♥♥ts ♦♦ ♣r♦t ♦ ♠t♥♦♠ strt♦♥s

♥t♦♥ t♥t ss ♠♦ t xi t drt t♦r rs♥ s♥t ♦♥ xi rs r♦♠ t t♥t ss ♠♦ t g ♦♠♣♦♥♥tst♥ ts ♣ s rtt♥ s ♦♦s

p(xi;θ) =

g∑

k=1

πkp(xi;αk) t p(xi;αk) =d∏

j=1

mj∏

h=1

(αjhk )xjhi ,

r θ = (π,α) r π s ♥ ♦♥ t s♠♣① ♦ s③ g rα = (α1, . . . ,αg)

♥ r αk = (α1k, . . . ,α

dk) s s tt αj

k = (αjhk ;h = 1, . . . ,mj) s ♥ ♦♥t s♠♣① ♦ s③ mj ♦t tt αjhk ♥♦ts t ♣r♦t② tt ♥ ♥rs♥ r♦♠ ♦♠♣♦♥♥t k ts ♠♦t② h ♦r r j

s♣t ts s♠♣t② t t♥t ss ♠♦ s t♦ ♦♦ rsts ♥ ♣rt❬❨❪ ♦r r♥t rs t ♦r s♥s ❬+❪ ♦r ♥ ♠♥❬+❪

♦ ♥tt② ♥r ♥tt② ♦ t t♥t ss ♠♦ s♣r♦ ② ♠♥ ts ♥ ♦s ❬❪ ❲ ♥♦ ♣rs♥t trt♦r♠ rr ♥trst ② ts ♣r♦♦ ♥ rr t♦ t rt ❬❪

♦r♠ ♥r ♥tt② ♦ t t♥t ss ♠♦ ❬❪ t t♠♦ ♥ ② ♥t♦♥ t d ≥ 3 ♣♣♦s tr ①sts tr♣rtt♦♥ ♦t st S = 1, . . . , d ♥t♦ tr s♦♥t ♥♦♥♠♣t② ssts S1 S2 S3 s tt κb =

j∈Sbmj t♥

min(g, κ1) + min(g, κ2) + min(g, κ3) ≥ 2g + 2.

Page 71: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss ♦ t♦r t sts stt ♦ t rt

♥ ♠♦ ♣r♠trs r ♥r② ♥t ♣ t♦ s♣♣♥ ♦r♦r t stt♠♥t r♠♥s ♥ t ♠①♥ ♣r♦♣♦rt♦♥s π r ① ♥♣♦st

♥s t t ♦♠tr ♣♣r♦s s s♦♥ ♥ ❬♦❪ t ♦♠tr♣♣r♦ ♦♦♥ ♦r t ♣rtt♦♥ ♥t♦ g sss ♠①♠③♥ t ♥♦r♠t♦♥ rtr♦♥♦r t χ2 rtr♦♥ s ♣♣r♦①♠t② q♥t t♦ ss♠ tt ♥s r r♥② t♥t ss ♠♦

Pr♠tr st♠t♦♥

♥r♥ ♦ t t♥t ss ♠♦ ♥ ♣r♦r♠ ♥ rq♥tst ♦r ♥ ②s♥ r♠♦r

♥ rq♥tst ♣♦♥t ♦ t st♠t♦♥ ♦ t ♠ ♥ ♣r♦r♠ ♥ ♠ ♦rt♠ ♣rs♥t ♦ ♦r ② ts ①t♥s♦♥s ♦t tt t ♦♦♥t♦♥ s ♣♣r♦♥ s♦ tr s ♥♦ ♥r② ♣r♦♠

♥ ②s♥ r♠♦r t st♠t♦♥ ♥ ♣r♦r♠ ② s s♠♣r♦t tt ② ♦♦s♥ t r②s ♥♦♥ ♥♦r♠t ♦♥t ♣r♦rs t ♣♦str♦rstrt♦♥s r ①♣t ♥ ♥ ①t ♥♦r♠t♦♥ rtr♦♥ ♥ ♦♠♣t

❲ ♥♦ t ♦t rq♥tst ♥ ②s♥ ♣♣r♦s

rq♥tst r♠♦r ♠ ♥ s② ♦t♥ ② t ♦♦♥ ♠

♦rt♠

trt♥ r♦♠ t ♥t ♦ θ[0] trt♦♥ [r] ♦ t ♠ ♦rt♠ srtt♥ s

st♣ t ♦♥t♦♥ ♣r♦ts

tik(θ[r]) =

π[r]k p(xi;α

[r]k )

p(xi;θ[r])

.

st♣ ♠①♠③t♦♥ ♦ t ①♣tt♦♥ ♦ t ♦♠♣tt ♦♦♦

π[r+1]k =

n[r]k

n♥ αjh[r+1]

k =

∑ni=1 tik(θ

[r])xjhi

n[r]k

,

r n[r]k =

∑ni=1 tik(θ

[r])

♦rt♠ ♦rt♠ ♦r t t♥t ss ♠♦

②s♥ r♠♦r ss ss♠♣t♦♥ ♦ t ♥♣♥♥ t♥ t♣r♦r strt♦♥s ♦ t ss ♣r♦♣♦rt♦♥s π ♥ ♦ t ss ♣r♠trs αj

k ♥♦s

Page 72: Model-based clustering for categorical and mixed data sets

♦♥r ♠①tr ♠♦s

tt

p(θ) = p(π)

g∏

k=1

d∏

j=1

p(αjk).

s t r②s ♥♦♥ ♥♦r♠t ♣r♦r ♦r ♠t♥♦♠ strt♦♥ s ♦♥trt ♦♥ t ♣r♦r strt♦♥ s rtt♥ s ♦♦s

p(π) = Dg

(

1

2, . . . ,

1

2

)

♥ p(αjk) = Dmj

(

1

2, . . . ,

1

2

)

.

♥r♥ s s♦ ♠ ② t ♦♦♥ s s♠♣r ♥rts sq♥ ♦ ♣r♠trs r♦♠ tr ♣♦str♦r strt♦♥s ♦t tt ts ♦rt♠s s② ♣r♦r♠ s♥ ♦♥t ♣r♦r strt♦♥s ♥♦ ①♣t ♣♦str♦r strt♦♥s

trt♥ r♦♠ t ♥t ♦ θ[0] trt♦♥ [r] ♦ t s s♠♣r♥ p(θ, ③|①) s stt♦♥r② strt♦♥ s rtt♥ s

∀i = 1, . . . , n z[r]i |xi,θ[r] ∼ Mg

(

ti1(θ[r]), . . . , tig(θ

[r]))

π[r+1]|z[r] ∼ Dg

(

1

2+ n

[r]1 , . . . ,

1

2+ n[r]

g

)

∀(k, j) αj[r+1]k |x, z[r] ∼ Dmj

(

1

2+ n

j1[r]k , . . . ,

1

2+ n

jmj [r]k

)

,

r ♥[r]k =

∑ni=1 z

[r]ik ♥ ♥jh[r]k =

∑ni=1 z

[r]ik x

jhi

♦rt♠ s s♠♣r ♦r t t♥t ss ♠♦

①t rtr♦♥ ② s♥ t ♣r♦♣rts ♦ t ♦♥t ♣r♦r strt♦♥s❬❪ ♣r♦♣♦s ♥ ①t rs♦♥ ♦ t rtr♦♥ ♦r t t♥t ss ♠♦♥ t ♥trt ♦♠♣tt ♦♦ ♦ ts ♠♦ ♥ ②

p(①, ③) =

θ∈Θ

p(①, ③;θ)p(θ)dθ,

s ①♣t ② s♥ t ♣r♦r strt♦♥s ♥ ♥ ♦r ♥② ♦♣ (①, ③)t ♥trt ♦♠♣tt ♦♦ s q t♦

p(①, ③) =Γ(g

2)

Γ(12)g

∏gk=1 Γ(♥k +

12)

Γ(n+ g2)

g∏

k=1

d∏

j=1

Γ(mj

2)

Γ(12)mj

∏mj

h=1 Γ(♥jhk + 1

2)

Γ(♥k +♠j

2)

.

❱t♦r ③ s r♣ ♥ t ♦ qt♦♥ ② ts ♠①♠♠ ♦♦ st♠t ③② s♥ t ♠♣ r ♥ t ①t rtr♦♥ s ♥ s ♦♦s

① = ln p(①, ③).

Page 73: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss ♦ t♦r t sts stt ♦ t rt

♥ ❬❪ t t♦rs ♣r♦♣♦s t♦ s ♥ ♦rr t♦ ♦♠♣t t ♦♠♣tt ♦♦ ② s♥ ♠♣♦rt♥ s♠♣♥ ♣♣r♦ ② ♥r♥ ② tr♥♠r ①♣r♠♥ts tt t ①t rtr♦♥ ♦t♣r♦r♠s t ss s②♠♣t♦t♥♦r♠t♦♥ rtr ♥ s ♥ t ①t rtr r t② t♦ ♦r

♣♣r♦ ♠ r st♠t ♦r r♥t ♥♠rs ♦ sss ♥t rtr♦♥ s s t♦ st t st ♥♠r ♦ sss

♥tr♣rtt♦♥ st ♠♦ s t t♥t ss ♠♦ t tr ♦♠♣♦♥♥ts st♠t sss ♥ ♥tr♣rt s ♦♦s

♠♦rt② ss π1 = 0.72 r♦♣s t tt ♥♦s s s♦♥t str♦♥ ♣r♦t② ② t ♥tsts s ♣r♦t② s♣♣r t♥ ♦r t rst ♦r ♥tsts ♥ q t♦ ♦r tst ♦♥

s♦♥ ss π2 = 0.20 r♦♣s t tt ♠ s s♦♥ ②t rst ♦r ♥tsts t ♠♦r ♥rtt t♥ ♥ t ♣r♦s ss♣r♦t② t♥ ♥ t② r ♠ s r♦s② t st ♥tst t ♣r♦t②

tr ss π3 = 0.08 r♦♣s t tt ♠♥② r sr♦s s♣② ② t t ♥tst

♥♥♥ ①♠♣ t♥t ss ♠♦ str♥

Prs♠♦♥♦s rs♦♥s ♦ t t♥t ss ♠♦

♥♠r ♦ ♣r♠trs rqr ② t t♥t ss ♠♦ s q t♦

(g − 1) + gd∑

j=1

(mj − 1).

s ts ♥♠r s ♥r② str♦♥② s♠r t♥ t ♥♠r ♦ ♣r♠trsrqr ② t ♦♥r ♠♦ s q t♦

∏dj=1mj

♦r ttr sr♥ tr ♦ ♥ ♦t♥ ② r♥ t ♥♠r♦ ♣r♠trs ♦r t t♥t ss ♠♦ s ♣rs♠♦♥♦s rs♦♥s ♦ tt♥t ss ♠♦ s ♥tr♦ ② ① ♥ ♦rt ❬❪ ♦r ♥r②rs t♥ ts ♠♦s s ①t♥ t♦ t t♦r rs ❬♦❪ ♦♥str♥ts ♦♥ t ♣r♠tr s♣ rqr t ♥tr♦t♦♥ ♦ ♥ ♠♦♣r♠tr③t♦♥ ❲t ts ♥ ♣r♠tr③t♦♥ t ♠t♥♦♠ strt♦♥ ♦r j ♦r ♦♠♣♦♥♥t k s tr♠♥ ② ts ♥tr a

jk ♥♦t♥ t ♠♦rt②

♠♦t② ♥ ts s♣rs♦♥ ♣r♠tr εjk

♥t♦♥ tr♥t ♣r♠tr③t♦♥ ♦ t ♣rs♠♦♥♦s t♥t ss ♠♦ t♥t ss ♠♦ ♥ ♣r♠tr③ s ♦♦s

p(xi;θ) =n∑

k=1

πk

d∏

j=1

(

(1− εjk)ajhk (εjk/(mj − 1))1−a

jhk

)xjhi,

Page 74: Model-based clustering for categorical and mixed data sets

♦♥r ♠①tr ♠♦s

s t 0 < εjk < 1 t ♣rs♠♦♥♦s ♠♦ ss♠s tt ♦♥ ♠♦ ♦rrs♣♦♥♥ t♦ t ♠♦st ② ♠♦t② s rtrst ♦r ♠t♥♦♠ tr♠♥♥ ♣r♦t② ♠ss s ♥♦r♠② s♣r ♠♦♥ t ♦tr ♠♦ts s♠♦ rqrs (g−1)+gd ♣r♠trs ♦tr ♣rs♠♦♥♦s ♠♦s r ♦t♥② ss♠♥ t qt② ♦ εjk t♥ t ss ♦r t♥ t rs ♦r t♥t ss ♥ t rs

♠ts ♦ t t♥t ss ♠♦

t♥t ss ♠♦ ♠② sr r♦♠ sr ss ♥ t t r ♥trss ♦rrt ♦r ♥st♥ ♥ ♣♣t♦♥ ♣rs♥t ♥ ❬❱❪ s♦s tt t♥tss ♠♦ r♠t② ♦rst♠ts t ♥♠r ♦ sss ♥ t ♦♥t♦♥♥♣♥♥ ss♠♣t♦♥ s ♦t ❲ ♥♦ ♣rs♥t tr tr♥t ♠①tr♠♦s r①♥ t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ ♦t tt t rr st ♥♠r ♦ rs t r s t rs t♦ ♦sr ♦♥t♦♥② ♦rrtrs ♥ t st ♥ ♦♥sq♥t② t r s t rs t♦ ♥♦ s ss② s♥ t t♥t ss ♠♦

♦♥r ♠①tr ♠♦s t ♥trss ♣♥♥

s

♥ ♦♥r ♠♦s ❬r❪ ♣r♣♦s s t♦ ♠♦③ t ♥ ♦♣r♦t② ② st♥ ♥trt♦♥s t♥ rs s t ♦♥r ♠①tr♠♦ s ♥ s ♦r ♦♥ t♠ ❬r ❪ t♦ str t♦r t stt ♥trss ♣♥♥s ♦t tt s♦♠ ♦♥str♥ts t♦ ♠♣♦s ♦♥ ♦♥r ♠♦ ♥ ♦rr t♦ ♦t♥ t ♠♦ ♥tt②

Page 75: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss ♦ t♦r t sts stt ♦ t rt

♣♣r♦ s♣♥ ♥ ♥♠♥ ❬❪ ♣♣② ♦♥r♠①tr ♠♦ t♦ t t t ♦t tt t♦rs st♠t sr ♠♦s① ② ♥ ♦s t st ♦♥ ♦♥srs ♠①tr t ♦r♦♠♣♦♥♥ts

♥tr♣rtt♦♥ rst t♦ ♦♠♣♦♥♥ts t ♥t♦ ♦♥t t ♥trt♦♥s t♥ t ♥tsts ♥ st t♦ ♦♠♣♦♥♥ts r s♣s♥ tr ♦ ♦♥② ♦♥ ♠♦t② ♥trt♦♥ ♥ t ♥♦ss rrs♣t② r♦s ♥ s♦♥

♦♠♠♥ts ♦t tt ts ss♠♣t♦♥s r rqr ② t t♦rs t♦ tr rst ♥tr ♥ ts ♠♦ ts t t ttr t♥ t♠ ♠♦ ♥ t ♦tr ♥ ts ♥tr♣rtt♦♥ ♥s t ♥②ss ♦ ♦rsss s♦ t t s♠♠r② s ♠♦r ♦♠♣① ♥② ♦ rt③t ♥ ♦ t t♦ s♣ sss ♠♦♥ ♦♥② ♦♥ ♠♦t② r♦ss♥♥ ts sss ♣♣rs s rt② ♥ ♦rr t♦ ♠♦③ t♦♥t♦♥ ♣♥♥s

♥♥♥ ①♠♣ ♦♥r ♠①tr ♠♦ str♥ ❬❪

♦ st♦♥ ② ♦♥sr♥ ♥trss ♣♥♥② ♦ ♦rr ♦♥ t t♦rs♦ ❬❪ ♦t♥ ♦♦ rsts ♦r t str♥ ♦ r♦r♣ r♦ss♥♦stss t♦rs ♣r♦r♠ t ♠♦ st♦♥ ② s♥ ♦rr ♠t♦ tr♠♥s t ♥trss ♥trt♦♥ ♦r ♥♦t tt ts ♣♣r♦ s s♦♣t♠♥ ♦♥rs t♦ ♦ ♦♣t♠♠ ♦ t ♥♦r♠t♦♥ rtr♦♥ s ② t ♣rtt♦♥r ♠♦ ♣rs♥t ♥ ❬❱❪ ♦♥srs t ♥trt♦♥s ♦ ♦rr t♦t r♥ ♥ t ♣♣t♦♥ tr r ♦♥② ♥trt♦♥s ♦ ♦rr ♦♥ rst♠t s ♦r t ♣r♦s rt t♦rs t♦ tr♠♥ ② ♥ t♥trss ♥trt♦♥s ♠♦ st♦♥ ♦r t ♦♥r ♠①tr ♠♦s s ♦♠♣① ♣r♦♠ s♥ t ♥♠r ♦ ♠♦s ♦♠s t t ♥♠r ♦rs

♦♦ ♠♥② ♣r♠trs ♥♠r ♦ ♣r♠trs rqr ② t ♦♥r♠①tr ♠♦ ♥rss t t ♥♠r ♦ ♠♦ts ♥ t t ♦♥sr♦rr ♦ ♥trt♦♥s s ts ♠♦ ♥ t t t t t ♠② ♥ t♦♦♠♥② ♣r♠trs ♦ tr s ♥ ♦rtt♥ rs ♥ t ♥tr♣rtt♦♥ ♦♠srr rtr♠♦r t ♣r♠trs ♥ ♣♦♦r② ♠♥♥ tr r t♦♦♥♠r♦s

♦♥s♦♥ ♦♥r ♠①tr ♠♦ s ♣♦r t♦♦ t♦ str t♦r t ♦r t s ♠♣♦rt♥t t♦ ♠♣♦s ♦♥str♥ts ♦♥ t ♣r♠trs s♣♥ ♦rr t♦ ♣r♦ ♠♥♥ ♠♦ ♦t ♠①tr ♠♦s ♣rs♥t ♥ ♣tr ♥ ♣tr ♥ ♥tr♣rt s ♦♥r ♠①tr ♠♦s t s♣

Page 76: Model-based clustering for categorical and mixed data sets

①trs ♦ trs

♦♥str♥ts ♦♥tr♦ t ♥♠r ♦ ♣r♠trs ♥ ♣r♦ ♠♥♥sss ♦t ♠♦s r ♥ t ♥ ♥t ♣♣r♦ t♦ ♣r♦r♠ t ♠♦st♦♥ ♥ ②s♥ r♠♦r

①trs ♦ trs

♣♥♥ trs

♥ s ♣♣r♦ ♣r♦♣♦s ② ♦ ♥ ❬❪ ♦♥ssts ♥♣♣r♦①♠t♥ srt ♠trt ♣r♦t② strt♦♥ t ♣♥♥ trs t ♣r♦t ♦ s♦♥♦rr strt♦♥s

♥t♦♥ P ♦ ♣♥♥ tr strt♦♥ t t tr T = E, V r E = 1, . . . , d ♥ V = (j, j′) : j ∈ E ♥ j′ ∈ E \ j r xi ss♠♣ r♦♠ ♣♥♥ tr strt♦♥ ♥ ② T t♥ ts ♣ s rtt♥s ♦♦s

p(xi;α) =

(j,j′)∈V p(xji ,x

j′

i ;βjj′)

∏dj=1 p(x

ji ;α

j)vj−1,

r α = (αj,βjj′

; j = 1, . . . , d; j′ s s (j, j′) ∈ V ) r vj ♥♦ts t r♥ ♦ t ♥♦r ♦ j ♣ ♦ ♦♠♣♦♥♥t k s ♥ ②

p(xji ;αj) =

mj∏

h=1

(αjh)xjhi ♥ p(xji ,x

j′

i ;βjj′) =

mj∏

h=1

mj′∏

h′=1

(βjj′h′h′)x

jhi xj

′h′

i ,

t αj = (αjh;h = 1, . . . ,mj) ♥ βjj′

= (βjj′hh′ ;h = 1, . . . ,mj;h

′ = 1, . . . ,mj′) ♣r♠tr αjh ♥♦ts t ♣r♦t② tt r j ts ♠♦t② h ♥ t♣r♠tr βjj

′hh′ ♥♦ts t ♣r♦t② tt t ♦♣ ♦ rs (j, j′) tst ♦♣ ♦ ♠♦ts (h, h′)

st♠t♦♥ s s♦♥ ♥ ❬❪ t ♠①♠♠ ♦♦ st♠t ♥ rt②♦t♥ ② s♥ t rs ♦rt♠ st♠ts t tr ♦ ♠♥♠ ♥t❬r❪ ♦ t r♥ t t♥ t t♦ r♥♦♠ rs Xj ♥Xj′ s ♥ ② t ♠t ♥♦r♠t♦♥ ♥ s

I(Xj,Xj′) =

mj∑

h=1

mj′∑

h′=1

p(Xjh = 1, Xj′h′ = 1) lnp(Xjh = 1, Xj′h′ = 1)

p(Xjh = 1)p(Xj′h′ = 1).

r♦♠ ts ♥t♦♥ t ♠♣r ♠t ♥♦r♠t♦♥ s ♦r s♠♣ ①

♥t♦♥ ♠♣r ♠t ♥♦r♠t♦♥ ♠♣r ♠t ♥♦r♠t♦♥ t♥ ①j = (xji ; i = 1, . . . , n) ♥ ①j

′= (xj

i ; i = 1, . . . , n) ♦♠♣t r♦♠ ts♠♣ ① s ♥ s

I(①j,①j′

) =

mj∑

h=1

mj′∑

h′=1

f(xjh, xj′h′) ln

f(xjh, xj′h′)

f(xjh)f(xj′h′),

r f(xjh, xj′h′) = 1

n

∑ni=1 x

jhi x

j′h′

i ♥ f(xjh) = 1n

∑ni=1 x

jhi

Page 77: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss ♦ t♦r t sts stt ♦ t rt

♦♠♣t I(①j,①j′

i ) ∀(j, j′) ♥① t d(d − 1)/2 r♥s ♦r♥ t♦ tr t ♦ t

t bℓ s rtr t♥ ♦r q t♦ t t bℓ′ ♥r j < j′

t b1 ♥ b2

♦r ℓ = 3 t♦ d(d−1)/2 t r♥ bℓ t ♦s ♥♦t ♦r♠ ②t t st ♣r♦s② st

♦rt♠ st♠t♦♥ ♦ t ♣♥♥ tr

♠r ❯♥q s♦t♦♥ t ts r r♥t t♥ t s♦t♦♥♦ ♦rt♠ s ♥q

r ♠①tr ♠♦

♥ s ♣♣r♦ ♣r♦♣♦s ② ♥ ♦r♥ ❬❪ ♥r③s t ♣r♦st trs t♦ t ♠①tr ♠♦ r♠♦r t♦rs ss♠tt ♦♠♣♦♥♥t ♦♦s strt♦♥ ♣r ♣♥♥ tr ♥ ♥

st♠t♦♥ ♥ rq♥tst r♠♦r t ♠ s s② ♦t♥ ② ♥ ♠

♦rt♠ ♠ st♣ ♠①♠③s t ①♣tt♦♥ ♦ t ♦♠♣t ♦♦ ②s♥ ♦rt♠ r t ♠♣r ♠t ♥♦r♠t♦♥ s ♦♠♣t ♦r♥t♦ t ♦♥t♦♥ ♣r♦ts ♦ t ss ♠♠rs♣s ♥ ②s♥ r♠♦rt ♠♣ s s♦ ♦t♥ ② s♣ ♠ ♦rt♠ ♠①♠③♥ t ♣♦str♦rstrt♦♥

♣♣r♦ ❲ str t t st t ♠①tr ♠♦s ♦ ♣♥♥②trs t r♥t ♥♠rs ♦ sss ♥ s t rtr♦♥ t♦ stt st ♦♥

♥tr♣rtt♦♥ st ♠♦ s t ♦♠♣♦♥♥t ♦♥ ♦t tt ts♠♦ rqrs t st♠t♦♥ ♦ ♣r♠trs t t♥t ss ♠♦rqrs ♦♥② ♣r♠trs ts rtr♦♥ s ttr t♥ t♦♠♣♦♥♥t t♥t ss ♠♦ rs♣t② ♥ ts ♦rst s ♥♦t ttr s♥ t tr♦♠♣♦♥♥t t♥t ss ♠♦ ♦t♥s rtr♦♥ ♦ t rtr♦♥ s r rt② ♦st ♣rtt♦♥s r r♥t s s♦♥ ② t ♦♥s♦♥ ♠trs ♣rs♥t ♥

♥♥♥ ①♠♣ r ♠①tr ♠♦ str♥

Page 78: Model-based clustering for categorical and mixed data sets

t t♥t ss ♠♦

tr tr

tr trtr tr tr

♦♥s♦♥ ♠trs t♥ t ♣rtt♦♥ ♦t♥ ② t ♦♠♣♦♥♥t♠①tr ♦ trs ♥ t ♣rtt♦♥ ♦t♥ ② t ♦♠♣♦♥♥t t♥t ss♠♦ t tr♦♠♣♦♥♥t t♥t ss ♠♦

♦♥s♦♥ ♠♥ ♣r♦♠ ♦ ts ♠♦s s tt t② rqr t♦♦ ♦t♥ ♥♥trt ♥♠r ♦ ♣r♠trs rtr♠♦r t tr strtr s ♦t♥ ♥st♥ t t st s tt t ♥ t♥ t tr strtr ♥ r②r♥t s t ♥tr♣rtt♦♥ s ♦♥ ts strtr ♥ rr♥t ♥②♥♦t tt t ♠①trs ♦ trs r ♠♥♥ ♣r♥♣② ♥ t tr strtr①♣♥s s♦♠ s rt♦♥s♣s

t t♥t ss ♠♦

♥ s t♦ ♦♥sr t♦ t♥t rs rst ♦♥ s t♦r♥ s rt t♦ t ss ♠♠rs♣ s♦♥ ♦♥ s ♦♥t♥♦s ♥rt♦r ♠trt ♥ ♠♦③s t ♥trss ♣♥♥s

r ♦ ts ♠t♦s ❲♥ ♦rts r t ♦♥t♦♥♣♥♥s t♥ t t♦r ♦♥s ♥ ♠♦ ② ♦st ♥t♦♥❬♦r ❲❪ ② ss♠♥ tt ts ♦rts r ♥♦sr t ♠tt♥t ss ♠♦ ❬❱r ❱r❪ ♥tr② ♥♦r♣♦rts t ♥trss ♣♥♥ss ♠♦ s ♦♥♥t♦♥s t t ♣♣r♦ ♦ ❨ ♥ ♥ t♥r ❬❪ r t ♥trss ♣♥♥s r ♠♦ ② t♥t ♦♥t♥♦sr t ♣r♦t ♥t♦♥ ②r ♠♦ ❬t❪ ♥ ♦r ss t♦r ♥②ss ♠♦ s tt t♦ tr t♦r rs ♦r t♦ t♦s t♦r rs ♥ ♣♥♥s s ♠♦r ♥r ♣♣r♦ ♥t② ♦♥♥ r♣② ❬❪ ♣r♦♣♦s t ♠①tr ♠♦ ♦ t♥t trts ♥②③rs ss♠s tt t strt♦♥ ♦ t t♦r rs ♣♥s ♦♥ ♦t t♦r t♥t r t ss ♥ ♠♥② ♦♥t♥♦s t♥t trts rs ♥r♥ s s♦ t ♣♦♥t s s♦ rt♦♥ ♣♣r♦ ts ♠♦s ♦♥sr t ♥trss ♣♥♥s tr ♠♥ r s ttts ♣♥♥s t♦ ♥tr♣rt ♠♦♥ rt♦♥s t t♥t rs ♣rt♥♥t ♥tr♣rtt♦♥ ♥ t

♦s ♦♥ t ❬❪ ♣♣r♦ ♠t t♥t ss ♠♦ ♣r♦♣♦s♥ ❬❪ s ♥tr♦ t♦ ♥②③ ♥r② t sts t ①♣♥s t ♥trss♣♥♥s ② ♦t ♥t♦♥

♥t♦♥ ❬❪ ♠t t♥t ss ♠♦ ♣ ♦ ♦♠♣♦♥♥t k

Page 79: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss ♦ t♦r t sts stt ♦ t rt

s rtt♥ s

p(xi;αk) =

R

d∏

j=1

Φ(akj + bkjt)xj1i (1− Φ(akj + bkjt))

1−xj1i dΦ(t),

r Φ(.) ♥♦ts t ♠t strt♦♥ ♥t♦♥ ♦ st♥r ♥♦r♠ r

♥ ♣rt ts ♣ s ♣♣r♦①♠t ② s♥ t ssr♠t qrtr ♠ s ♦t♥ ② s♥ ♥ ♠ ♦rt♠ ❲ ♥♦ ♣rs♥t t rst ♦ ts♠♦ ♦r t r♥♥♥ ①♠♣

♣♣r♦ s t st s♣ ♠♦ t ♦r sss ♣r♦♣♦s ♥❬❪ s♠s rt t t♦rs ♦ ❬❪ ♣rr t♦ s t r♥♦♠ts ♠♦s ♥ t♥t ss ♥②ss t t♦ sss ② ss♠ tt♦♥t♦♥ ♣♥♥s ♥ ♠♦③ ② s♥ ♦♥t♥♦s t♥tr rs ♠♦♥ t ♥s ♦r♥ t♦ t t♦rst t♥t ♦♥t♥♦s r ♥ rt t ♥♥ ♦ t ♦♥t♦♥ ♦♠s

♥tr♣rtt♦♥ ♦r♥ t♦ t t♦rs ♦♥ ss r♣rs♥ts t s♦♥tt ♥ t ♦tr r♣rs♥ts t r♦s ♦♥s r♥♦♠ t r♣rs♥ts t ♣t♥t s♣ ♥r♦r rtrsts ♦ t ①r② ♠sr ♠♦ ♦s ♥♦t rqr t t♦ t♦♥ rt sss str ♥tr♣rtt♦♥ s sr ♥ t s ♥♦t s② t♦ t t str♥t♦ t ♥trss ♣♥♥s

♥♥♥ ①♠♣ str♥ t t r♥♦♠ ts ♠♦

♦♥s♦♥ ② ♥ t♦ s ♦ t♥t rs t ♠t t♥t ss♠♦s ♣r♠ts t♦ ♦♥sr t ♥trss ♣♥♥s ♦r t s ♥♦t s② t♦rtr③ ts ♣♥♥s s♥ tr s ♥♦♥ ♣r♠tr rt♥ t str♥t♦ ts ♣♥♥s

♦♥s♦♥

ss t♥t ss ♠♦ s ♦t♥ s ♥ t s♠♣ s③ s r s ts ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ s ♦t r♥t ♠t♦s ♦t♦ str t t ② t♥ ♥t♦ ♦♥t t ♥trss ♣♥♥s ♦rtr s ♥♦t ♥② ♠♦ ♣r♦s ♦♥ ♦♥t t♦ rtr③ t str♥t ♦ts ♣♥♥s

♥ ts ♦r ♥♦t s♣♦♥ ♦t t ♠①tr ♦ t♦r ♥②③rs s♥ ♥t t♦ ♦r t ♠♦s r s② ♥tr♣rt ♦ ♦s ♦♥♠♦s str t ♥s ② ♠♦♥ t strt♦♥ ♦ t rs♥ tr ♥t s♣

Page 80: Model-based clustering for categorical and mixed data sets

♦♥s♦♥

♦♥r ♠①tr ♠♦s s♠s t♦ t ♠♦st ♥r ♦♥ ❲ s♦ ♣r♦♣♦s♥ t t♦ ♦♦♥ ♣trs t♦ ♠①tr ♠♦s s♣ ♦♥str♥ts t♦ts ♥r ♠♦ s ♦t ♣r♦♣♦s ♠♦s ♦ t♦ s♠♠r③ t ♥trss♣♥♥s t ♣r♠trs

Page 81: Model-based clustering for categorical and mixed data sets
Page 82: Model-based clustering for categorical and mixed data sets

♣tr

♦s str♥ t ♦s

♦ ①tr♠ strt♦♥s

s ♣tr ♥tr♦s ♥ ①t♥s♦♥ ♦ t t♥tss ♠♦ s ♠♦ r♦♣s t rs ♥t♦ ♦♥t♦♥② ♥♣♥♥t ♦s s♣ strt♦♥♦ t ♦s ♠♦③s t ♥trss ♣♥♥s ♥♣r♦s ♦♥ ♦♥t s♠♠r③♥ t str♥t ♦ts ♣♥♥s ♠①♠♠ ♦♦ ♥r♥ s ♣r♦r♠ ② ♠♦rt♠ t ♦♠♥t♦r ♣r♦♠s ♦ t ♠♦st♦♥ r ♦ ② ♠♠ ♦rt♠♠r ①♣r♠♥ts ♦♥ s♠t ♥ r t sts♥r♥ t ♠♥ rtrsts ♦ ts ♥ ♠①tr♠♦

♥ ♥r s♦s ♣r♦♠ t♦trt♥ t♥ ♠♦r

♦r r♥r

♥tr♦t♦♥

❲ ♣r♦♣♦s t♦ ①t♥ t ss t♥t ss ♠♦ ♦r t♦r t ② ♥ ♠①tr ♠♦ r①s t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ t♥t rs ❲ rr t♦ t ♣r♦♣♦s ♠♦ s t ♠①tr ♦ ①tr♠ ♣♥♥②strt♦♥s ♣r ♦s ♥♦t ② ♠

♠♠♦ r♦♣s t rs ♥t♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♥t ss ♠♥ ♥trss ♣♥♥s r ts ♥r♥ ② t r♣rtt♦♥♦ t rs ♥t♦ ts ♦s s ♣♣r♦ ♦♥ ♠♦♥ ♦ t ♠♥ ♦♥t♦♥ ♥trt♦♥s s rst ♣r♦♣♦s ② ♦r♥s♥ ♥ ♥t ❬ ❪♥ ♦rr t♦ str t sts t ♦♥t♥♦s ♥ t♦r rs ♦r t ♠♠♦ ♦ ♦♦s ♣rtr ♣♥♥② strt♦♥ ♦♥ssts ♥ ♦♠♣♦♥♥t ♠①tr ♦ t ♥♣♥♥ ♥ t ♠①♠ ♣♥♥② strt♦♥ ♦r♥ t♦ t r♠rs ❱ rtr♦♥ s s♣ strt♦♥ ♦ t ♦s

Page 83: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s

♣r♦s ♦♥ ♣r♠tr s♠♠r③♥ t str♥t ♦ t ♦♥t♦♥ ♣♥♥s ♦t rs s r ♣r♠tr s t ♣r♦♣♦rt♦♥ ♦ t ♠①♠♠ ♣♥♥②strt♦♥ rtr♠♦r t ♥tr ♦ t ♦♥t♦♥ ♣♥♥s s r♥ ♦t② t rt♦♥ ♥ ② t ♠①♠♠ ♣♥♥② strt♦♥ s t ♠♦♣ts t t ♦♥ t ♠♥ ♦♥t♦♥ ♣♥♥s ♥ tr str♥ts

♣r♦♣♦s ♠♦ ♥ ♥tr♣rt s t♦ ♣rs♠♦♥♦s rs♦♥ ♦ ♦♥r ♠①tr ♠♦ ♥ ts ♥ts r♦♠ ts ♥tr♣rtt ♣♦r rst ♥s t ♦♥sr ♥trt♦♥s ② r♦♣♥ ♥ t s♠ ♦ t rs r ♦♥t♦♥② ♣♥♥t str♥t ♦ ts ♣♥♥② s rt② t ♣r♦♣♦rt♦♥ ♦ t strt♦♥ ♦ ♠①♠♠ ♣♥♥② ♦♠♣r t♦ tt ♦t ♥♣♥♥ strt♦♥ s♦♥ ♦ s♣rst② s ♥ ② t s♠rt♦♥ ♦ t ♣r♠trs ♦ t ♠①♠♠ ♣♥♥② strt♦♥ ♦ t ♦s ♦r ♦♥r ♠①tr ♠♦s t st♦♥ ♦ t ♣rt♥♥t ♥trt♦♥s s ♦♠♥t♦r ♣r♦♠ r♦r ♣r♦♣♦s t♦ ♣r♦r♠ t ♠♦ st♦♥ ♠♠ ♦rt♠ ♥ ♦rr t♦ ♦ t ♥♠rt♦♥ ♦ t ♠♦s sts ♥r ♣♣r♦ ♦ s♦ st t ♥trt♦♥s ♦ ♠♦r ♥r ♦♥r♠①tr ♠♦

trtr ♦ ts ♣tr s ♣tr s ♦r♥③ s ♦♦s t♦♥ ♣rs♥ts t ♠①tr ♠♦ ♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♦ rs t♦♥ ♣rs♥ts t ♥ ♠①tr ♠♦ t♥ ♥t♦ ♦♥t t ♥trss ♣♥♥s t♦♥ s ♦t t♦ t st♠t♦♥ ♦ t ♣r♠trs ② ♠①♠③t♦♥♦ t ♦♦ ♥ t s r t ss ♥♠r ♥ t ♦s ♦ rsr s♣♣♦s t♦ ♥♦♥ t♦♥ ♣rs♥ts ♠♠ ♦rt♠ ♦♥ ♦♠♥t♦r ts ♥r♥t t♦ ♦ st♦♥ t♦♥ ♣rs♥ts rsts ♦♥s♠t t t♦♥ strts t ♠ ♠♦ ♦♥ t♦ r str♥ ♥s ♦♥s♦♥ s ♥ ♥ t♦♥ ♦t tt tt♦r ♦ t ♣strt ♣r♦r♠♥ t ♠♦ st♦♥ ♥ t st♠t♦♥ ♦ t ♣r♠trs♦ ♠ s ♥ ♥ ♣tr ts rsts r ♣rt ♦ t rt ♦sstr♥ ♦r ♦♥t♦♥② ♦rrt t♦r t ❬❱❪

①tr ♦ ♥trss ♥♣♥♥t ♦s

♥ ♠①tr ♠♦ ♦ ♥trss ♥♣♥♥t ♦s ♦♥srs tt♦♥t♦♥② ♦♥ ss k rs r r♦♣ ♥t♦ k ♥♣♥♥t ♦s ♥ ♦ ♦♦s s♣ strt♦♥

♣rtt♦♥ ♦ t rs ♣r ss r♣rtt♦♥ ♦ t rs ♥t♦♦s tr♠♥s ♣rtt♦♥ σk = (σk1, . . . ,σkk) ♦ 1, . . . , d ♥ k s♦♥t ♥♦♥♠♣t② ssts r σkb r♣rs♥ts t sst b ♦ rs ♥ t ♣rtt♦♥ σks ♣rtt♦♥ ♥s t t♦r ♦ t♦r rs xkb

i = xσkbi = (x

kbji ; j =

1, . . . , dkb) s t sst ♦ xi ss♦t t♦ σkb ♥tr dkb = r(σkb)

♣ strt s ♦♥ ♦r st t t ♦♦♥ r tt♣sr♦rr♣r♦t♦rr♦♣❴

Page 84: Model-based clustering for categorical and mixed data sets

①tr ♦ ♥trss ♥♣♥♥t ♦s

s t ♥♠r ♦ rs t t♦ ♦ b ♦ ♦♠♣♦♥♥t k t♦r xkbji =

(xkbjhi ;h = 1, . . . ,m

kbj ) ♦rrs♣♦♥s t♦ r j ♦ ♦ b ♦r ♦♠♣♦♥♥t k ♥

ss ♦♠♣t s♥t ♦♥ r mkbj s t ♥♠r ♦ ♠♦ts ♦r t

r xkbji s xkbjhi = 1 ♥ i ts ♠♦t② h ♦r r x

kbji

♥ xkbjhi = 0 ♦trs

♠r r♥t ♥trss ♣♥♥s r♥t rs r♣rtt♦♥s ♥♦s r ♦ ♦r ♦♠♣♦♥♥t ♥ t② r r♦♣ ♥t♦ σ = (σ1, . . . ,σg)

♥t♦♥ ①tr ♠♦ ♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♦ rst xi t♦ t drt t♦r r rs♥ r♦♠ ♠①tr ♠♦ ♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♦ rs ♦s t ♣rtt♦♥ s ♥♦t ② σ ♥t ♣r♠trs ② θ ♥ ts ♣ s rtt♥ s ♦♦s

p(① i;σ,θ) =

g∑

k=1

πkp(① i;σk,αk) t p(① i;σk,αk) =

k∏

b=1

p(①kbi ;αkb),

r αk = (αk1, . . . ,αkk) ♥ r p(① kbi ;αkb) s t ♣ ♦ t ♦ b ♦ t

♦♠♣♦♥♥t k ♣r♠tr③ ② αkb

①♠♣ ♦♠♣♦♥♥t ♠①tr ♠♦ ♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♦rs t xi = (x1

i , . . . ,x5i ) t t♦r ♦ t♦r rs ♦♦♥

t ♦♠♣♦♥♥t ♠①tr ♠♦ ♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♣rtt♦♥♦ t rs ♦ ts ♠♦ s σ = (σ1,σ2) t σ1 = (1, 2, 3, 4, 5) ♥σ2 = (1, 5, 2, 4, 3) r strts t ♥trss ♣♥♥s t♥♥t♦ ♦♥t ② t ♠♦ ♥ ♥ts tt t ♥trss ♦rrt♦♥ s♥t ♥ ♥ts tt ts ♦rrt♦♥ s t♥ ♥t♦ ♦♥t

X5

X4

X3

X2

X1

X1

X2

X3

X4

X5

x11

x12

ss

X3

X4

X2

X5

X1

X1

X5

X2

X4

X3

x21

x22

x23

ss

r ♥trss ♣♥♥s t♥ ♥t♦ ♦♥t ② t ♦♠♣♦♥♥t♠①tr ♠♦ ♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♦ rs t σ1 =(1, 2, 3, 4, 5) ♥ σ2 = (1, 5, 2, 4, 3)

♦t tt t ss t♥t ss ♠♦ t ♦♥t♦♥ ♥♣♥♥ ♦ r♣rs♥t ② t s ♦ t ♦♥ ♥ ♦♥ t ttr

Page 85: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s

t ♠♦s ♣♣r♦ ♣r ♦♥t♦♥② ♥♣♥♥t ♦s s r② ♥r s♥ ♥② strt♦♥ ♥ ♦s♥ ♦r ♦ strt♦♥ p(① kb

i ;αkb) ♠①tr ♠♦ ② ♦♥t♦♥ ♥♣♥♥t ♦s s ♣rs♠♦♥♦s rs♦♥♦ t ♦♥r ♠①tr ♠♦ ♥ t strt♦♥ ♦ ♦ tr♠♥s ♥trt♦♥s r ♦♥sr ♦t tt t ♦rr ♦ ts ♥trt♦♥s s tr♠♥ ② t ♥♠r ♦ t rs ♥t♦ t ♦ ♥② t ♥trt♦♥st♥ rs ♦ r♥t ♦s ③r♦ ♥ t♦s t♥ rs ♦ ts♠ ♦ ♥ ♠♦③ ② t s♣ strt♦♥ ♦ t ♦ ♠t♥s ♦ ts ♠♦ r k = d ♦r ss s q♥t t♦ t t♥t ss ♠♦

♥r ♥tt② ♥r ♥tt② ♦ t ♠①tr ♠♦s ♦r t♦r t ♥ t② ♣r♦ ♦r ② ♥ s♦♠ ♦♥str♥ts ♦♥t r♣rtt♦♥ ♦ t rs ♥t♦ ♦s ♥ s ♦r♠ ❬❪s t ♥r ♥tt② ♦ t ♠♦ s ♦t♥ ② s♥ ts ♦♥t♦♥♥♣♥♥ ss♠♣t♦♥ t♥ ♦s ♥r t♦ s♥t ♦♥t♦♥s

♦r♦r② ♥r ♥tt② ♦ t ♠①tr ♠♦ ♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♦ rs q t♥ sss σ1 =, . . . ,= σg t 1 ≥ 3

♥ tt t ♦ strt♦♥s p(①kbi ;αkb) r ♥t ♥ υb rs ♦

r♦♠ t♥ s♣♣♦s tr ①sts tr♣rtt♦♥ ♦ t st S = σ11, . . . ,σ11 ♥t♦tr s♦♥t ♥♦♥♠♣t② ssts S1 S2 ♥ S3 s tt κu =

j∈Suυj t♥

min(g, κ1) + min(g, κ2) + min(g, κ3) ≥ 2g + 2.

♥ ♠♦ ♣r♠trs r ♥r② ♥t ♣ t♦ s♣♣♥

Pr♦♦ r s t♦♥ r♦♠ xkbi t♦ x

kbi r x

kbi s t♦r r

♥ υb ♠♦ts r xkbi ♦♦s t t♥t ss ♠♦ s♦ ts ♥t

t② s ♥ ② ♦r♠ s ♦♥ t♦ t ♥r ♥tt②♦ t ♠♦ r♥ x

kbi

♦r♦r② ♥r ♥tt② ♦ t ♠①tr ♠♦ ♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♦ rs tr ①sts tr♣rtt♦♥ ♦ σk qs ♦r k = 1, . . . , g ♥t♦ tr s♦♥t ♥♦♥♠♣t② ssts S1 S2 S3

∀k ∈ 1, . . . , g, ∀σkb ∈ σk, ∃u ∈ 1, 2, 3 s σkb ∈ Su,

♥ tt t ♦ strt♦♥s p(①kbi ;αkb) r ♥t ♥ υb rs

♦ r♦♠ s tt κu =∏

j∈Suυj t♥

min(g, κ1) + min(g, κ2) + min(g, κ3) ≥ 2g + 2.

♥ ♠♦ ♣r♠trs r ♥r② ♥t ♣ t♦ s♣♣♥

Pr♦♦ ♣r♦♦ s s♠r t♥ t ♣r♦♦ ♦ ♦r♦r② ♦t tt t ①st♥♦ xkb

i s ssr ② t qt② ♦ t tr♣rtt♦♥ ♦ t σk t♥ sss

Page 86: Model-based clustering for categorical and mixed data sets

Prs♠♦♥♦s ♦ strt♦♥

Prs♠♦♥♦s ♦ strt♦♥

♥ ♠ s t♦ ♥ ♣rs♠♦♥♦s strt♦♥ ♦r ♦ ttts ♥t♦ ♦♥t t ♣♥♥② t♥ rs rtr♠♦r t ♣r♠trs♦ t strt♦♥ ♥s ♦ ♠st ♠♥♥ ♦r t ♣rtt♦♥r ♥ ts♦♥t①t ♣r♦♣♦s t♦ ♠♦③ t strt♦♥ ♦ ♦ ② ♠①tr ♦ tt♦ ①tr♠ strt♦♥s ♦r♥ t♦ t r♠rs ❱ rtr♦♥ ♦♠♣t ♦♥ t ♦♣s ♦ rs ♠♦ rsts ♥ ♦♠♣♦♥♥t ♠①tr t♥ ♥♥♣♥♥ strt♦♥ ♥ ♠①♠♠ ♣♥♥② strt♦♥ ♥ s② ♥tr♣rt ② t sr

♠①♠♠ ♣♥♥② strt♦♥ s ♥tr♦ rst t♥ t ♠①tr♠♦ ♦ ①tr♠ ♣♥♥② strt♦♥s ♣r ♦s ♠ s s♦♥② t

♠r rr rs ❲t♦t ♦ss ♦ ♥rt② t rs r ♦♥sr s ♦rr ② rs♥ ♥♠r ♦ ♠♦ts ♥ ♦

∀(k, b) mkbj ≥ m

kbj+1 r j = 1, . . . , dkb − 1.

①♠♠ ♣♥♥② strt♦♥

♥ ♠①♠♠ ♣♥♥② strt♦♥ s ♥ s t ♦♣♣♦st strt♦♥ ♦ ♥♣♥♥ ♦r♥ t♦ t r♠rs ❱ rtr♦♥ ♦♠♣t ♦♥ t♦♣s ♦ rs ♥ t ♥♣♥♥ strt♦♥ ♠♥♠③s ts rtr♦♥ t ♠①♠♠ ♣♥♥② strt♦♥ ♠①♠③s t ❯♥r ts strt♦♥t ♠♦t② ♥♦ ♦ ♦♥ r ♣r♦s t ♠①♠♠ ♥♦r♠t♦♥ ♦♥ t ssq♥t rs

♠r ♦♥r♣r♦ ♥t♦♥ ♥ ♦t tt t s ♥♦♥r♣r♦ ♥t♦♥ ♥ t♥ rs ♥ x

kbi rss r♦♠ ts strt♦♥ t

♥♦ ♦ t r ♥ t rst ♥♠r ♦ ♠♦ts tr♠♥s ①t② t ♦trs t t rrs ♦s ♥♦t ♥ssr② ♣♣②

♠r ss srt♦♥s s strt♦♥ ♥s sss srt♦♥s r♦♠ t s♣ ♦ xkbji t♦ t s♣ ♦ xkbj+1

i t j = 1, . . . , dkb−1 rtt t rs r ♦rr ② rs♥ ♥♠r ♦ ♠♦ts ♥ ♦ ♥t t s r♣r♦ ♥t♦♥ ♥ ♦♥② ♥ mkb

j = mkbj+1

Pr♠tr③t♦♥ ♥ t rst r tr♠♥s t ♦tr ♦♥s ts strt♦♥ s ♥ ② ♣r♦t t♥ t ♠t♥♦♠ strt♦♥ ♦ t rstr ♣r♠tr③ ② t ♦♥t♥♦s t♦r

τ kb = (τhkb;h = 1, . . . ,mkb1 ) t τhkb ≥ 0 ♥

mkb1∑

h=1

τhkb = 1,

♥ t ♣r♦t t♥ t ♦♥t♦♥ strt♦♥s ♥ s s♣ ♠t♥♦♠strt♦♥s ♦ ♦♥t♦♥② ♦♥ x

kb1hi = 1 t♥ ♦r j = 2, . . . , dkb x

kbji

Page 87: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s

♦♦s ♠t♥♦♠ strt♦♥ ♣r♠tr③ ② t srt t♦r

δhjkb = (δhjh

kb ;h′ = 1, . . . ,mkbj ) t δhjh

kb ∈ 0, 1,m

kbj∑

h′=1

δhjh′

kb = 1 ♥m

kb1∑

h=1

δhjh′

kb ≥ 1.

♦t tt t ♦ ♦♥str♥ts ♥ t sss srt♦♥s ② ♥♦t♥ δkb =(δhjkb ;h = 1, . . . ,m

kb1 ; j = 2, . . . , dkb) t strt♦♥ ♦ ♠①♠♠ ♣♥♥②

♥ ♥♦ ♥

♥t♦♥ ①♠♠ ♣♥♥② strt♦♥ t xkbi t dkbrt

t♦r r ♦♦♥ t ♠①♠♠ ♣♥♥② strt♦♥ ♦s t srt ♣r♠trs r ♥♦t ② δkb ♥ ♦s t ♦♥t♥♦s ♦♥s r ♥♦t ②τ kb ♥ ts ♣ s rtt♥ s ♦♦s

p(xkbi ; τ kb, δkb) = p(x

kb1i ; τ kb)

dkb∏

j=2

p(xkbji |xkb1

i ; δhjkbh=1,...,mkb1

)

=

mkb1∏

h=1

(

τhkb

dkb∏

j=2

mkbj∏

h′=1

(δhjh′

kb )xkbjh′

i

)xkb1hi

.

①♠♣ rt ♥ trrt ♠①♠♠ ♣♥♥② strt♦♥s tt ♠①tr ♠♦ ♦s t ♦s ♦ rs ♦r t rst ♦♠♣♦♥♥t r ♥② σ1 = (1, 2, 3, 4, 5) strt♦♥s ♦ t ♦s r ♠①♠♠ ♣♥♥②♦♥s ♦s t ♣r♠trs r t ♦♦♥

δ11111 = δ21211 = δ31311 = δ41311 = δ1j112 = δ2j212 = 1,

τ 11 = (0.1, 0.3, 0.2, 0.4) ♥ τ 12 = (0.5, 0.5).

r s♣②s t ♣r♦ts ♦ t ♦♥t strt♦♥s ② t r ♦ r♦①s ♦t tt δkb ♥s t ♦t♦♥s r t ♣r♦ts r ♥♦♥ ③r♦ ♦t♦♥ ♦ r ♦①s ♥ τ kb ♥s t ♣r♦ts ♦ ts ♥♦♥ ③r♦ s r ♦t r ♦①s

♥tt② s♥t ♦♥t♦♥ ♦ ♥tt② s t♦ ♠♣♦s τhkb > 0 ♦r h = 1, . . . ,m

kb1 s strt♦♥ s r② ♠t ♥trst s t s s♦

♥rst tt t ♥ ♠♦st ♥r s ♦♥ ♥♦ ♣rs♥t ♦ t♦ s t ♥ ♠♦r ♥t ②

♦ strt♦♥ ♠①tr ♦ t♦ ①tr♠ str

t♦♥s

♥ ❲ ss♠ tt t ♦s ♦♠♣♦s ② t st t♦ rs ♦♦ ♦♠♣♦♥♥ts ♠①tr t♥ ♥ ♥♣♥♥ strt♦♥ ♥ ♠①♠♠ ♣♥♥② strt♦♥ t ♦ ♦♠♣♦s ② ♦♥ r ♦♦ ♠t♥♦♠strt♦♥

Page 88: Model-based clustering for categorical and mixed data sets

Prs♠♦♥♦s ♦ strt♦♥

τ11

1

τ11

2

τ11

3τ11

4

x1111

x1112

x1113

x1114

x1123

x1122

x1121

rst ♦ ♦ ss ♦♥ ♦ ♦ ss

r ♦ ①♠♣s ♦ ♦ strt♦♥s ♦♦♥ ♠①♠♠ ♣♥♥②strt♦♥ r m11

1 = 4 m112 = 3 ♥ m12

1 = m122 = m

123 = 2

♥t♦♥ ♦s str♥ ♦ ♦s ♦ ①tr♠ strt♦♥s drt t♦r r xi s ♥rt ② ♠ ♠♦ t s r♥ ② ♠①tr ♠♦ ♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♦s t ♣ s rtt♥ s♦♦s

p(① i;σ,θ) =

g∑

k=1

πk

k∏

b=1

p(①kbi ;αkb).

♦r♦r t ♣ ♦ ♦ b ♦r ♦♠♣♦♥♥t k s rtt♥ s

p(①kbi ;αkb) =

(1− ρkb)p(①kbi ; ξkb) + ρkbp(①

kbi ; τ kb, δkb) dkb > 1

p(①kbi ; ξkb) ♦trs

r p(① kbi ; τ kb, δkb) s t ♣ ♦ t ♠①♠♠ ♣♥♥② strt♦♥ ♥

② ♥ r p(① kbi ; ξkb) s t ♣ ♦ t ♥♣♥♥ strt♦♥ ♥

② p(① kbi ; ξkb) =

∏dkb

j=1

∏mkbj

h=1 (ξjhkb )xkbjhi ♣r♠tr αkb = (ρkb, ξkb, τ kb, δkb)

r♦♣s t ♣r♠trs ♦ ♦ b ♦r ♦♠♣♦♥♥t k ♥② t r ρkb ∈ [0, 1] st ♣r♦♣♦rt♦♥ ♦ t ♠①♠♠ ♣♥♥② strt♦♥

♠r ♦ ♣r♠trs ♠ ♠♦ rqrs tt t♦♥ ♣r♠trs♦♠♣r t t ♠ ♠♦ ♥ ♦r ♦ t t st t♦ rst ♥♠r ♦ t♦♥ ♣r♠trs ♣♥s ♦♥② ♦♥ t ♥♠r ♦ ♠♦ts ♦t rst r ♦ t ♦ ♥ ♥♦t ♦♥ t ♥♠r ♦ rs ♥t♦ t ♦ ♥♠r ♦ ♣r♠trs ♦ ♠ ♥♦t ② ν♠ s s♦ ♥ ②

ν♠ = (g − 1) + gd∑

j=1

(mj − 1) +∑

(k,b)|dkb>1

mkb1 .

♥ t♦♥ t ♠ ♠♦ s s② ♥tr♣rt s ①♣♥ ♥ t ♥①t♣rr♣ ♦t tt t ♠t♥ s r ρkb = 0 ♥s t ♦ strt♦♥② t ♥♣♥♥ ♦♥ ♥ ts ♣rtr s t ♣r♠trs ♦ t ♠①♠♠♣♥♥② strt♦♥ r ♥♦ ♦♥r ♥

Page 89: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s

♥♥ ♦ strt♦♥ ❯♥r ts strt♦♥ t ♣r♦♣♦rt♦♥ ♦ t♠①♠♠ ♣♥♥② strt♦♥ rts t t♦♥ r♦♠ ♥♣♥♥ ♥rt ss♠♣t♦♥ tt t tr♥t strt♦♥ s t ♠①♠♠ ♣♥♥② strt♦♥ ♣r♠tr ρkb s ♥ ♥t♦r ♦ t ♥trr ♣♥♥②♦ t ♦ t s ♥♦t r ♣rs ♣♥♥② ♠♦♥ rs t ♣♥♥② t♥ rs ♦ t ♦ rtr♠♦r t st②s ♦♥ ♥ t♥♠r ♦ rs s rr t♥ t♦ t r♠rs ❱ s ♥♦♥ ♣♣r♦♥♥ ts s ♥trr ♣♥♥s r ♥ ② δkb str♥t ♦ts ♣♥♥s s ①♣♥ ② τ kb ♥ ts t♦r s t t ♦ t♦rr♣rs♥t ♠♦t② r♦ss♥s ♦♠♣r t t ♥♣♥♥ strt♦♥

Prs♦♠♥♦s ♦♥r ♠①tr ♠♦ ❲ ♥tr♣rt t ♠ ♠♦ s t♦ ♣rs♠♦♥♦s rs♦♥ ♦ t ♦♥r ♠①tr ♠♦ rst ♦♥ s♥ ② t r♣rtt♦♥ ♦ t rs ♥t♦ ♦s tr♠♥♥ t ♦♥t♦♥♥trt♦♥s t♦ ♠♦ s♣ ♦ strt♦♥ s s♦♥ ♦ ♣rs♠♦♥② s♥ ♠♦♥ t ♥trt♦♥s ♦ ② ♦♠♣♦♥♥t ♦♥② t♦s♦rrs♣♦♥♥ t♦ t ♠①♠♠ ♣♥♥② strt♦♥ r ♠♦ t ♦tr♦♥s r ♦♥sr s ♥

♥tt② ♣r♦♣♦s strt♦♥ s ♥t ♥r t ♦♥t♦♥ ttt ♦ s ♦♠♣♦s ② t st tr rs dkb > 2 ♦r tt t ♠♦t②♥♠r ♦ t st r ♦ t ♦ s rtr t♥ t♦ mkb

2 > 2 s rst s♠♦♥strt ♥ ♣♣♥① ❲ r♠♥ tt t ♣r♠tr ρkb s ♥ ♥t♦r♦♥ t♦ ♠sr t ♣♥♥② t♥ rs ♥♦t ♠t t♦ ♣♥♥②t♥ ♦♣s ♦ rs ♦r dkb = 2 ♥ m

kb2 = 2 t♥ t ♦

strt♦♥ s ♥♦t ♥t s♦ ♥ ♦♥str♥t s ♥ ♦rr t♦ t♠♦st ♠♥♥ ♣r♠trs t ♦s♥ ♦ ρkb s t rst ♠①♠③♥t ♦♦♦ s t♦♥ ♦♥str♥t ♦s ♥♦t s② t ♥t♦♥ ♦ ρkbs ♥ ♥t♦r ♦ t ♣♥♥② str♥t t♥ t rs ♦ t s♠ ♦rtr♠♦r ts ♦♥str♥t s ♥tr s♥ ♦s t t st ♣♥♥sr ♥t ♦t tt ρkb s♠s t♦ ♦rrt t t r♠rs ❱ s strt② t ♦♦♥ ①♠♣

①♠♣ r♠rs ❱ ♥ ρkb t♦ ♠srs ♦ t ♣♥♥② r ♣rs♥ts t ♥ t♥ t r♠rs ❱ ♥ ρkb ♦♥ s♠t rt ♥r②rs ♦r s s♦ ♥ ♦sr ♥ ♠♥② ♦tr stt♦♥s

①♠♠ ♦♦ st♠t♦♥

♦rt♠

♠ t ① = (① 1, . . . ①n) t s♠♣ ♦♠♣♦s t n ♥♣♥♥t ♥ ♥t② strt ♥s ss♠ t♦ rs r♦♠ t ♠ ♠♦ r♦♠ tss♠♣ t ♠ s t♦ st♠t t ♠ ♦r ① ♠♦ m ♥ ② (g,σ)

Page 90: Model-based clustering for categorical and mixed data sets

①♠♠ ♦♦ st♠t♦♥ ♦rt♠

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Cramer’s V

ρkb

r ♦t♦♥ ♦ ρkb ♦♠♣t t t ♥tt② ♦♥str♥t ♦r♥t♦ t r♠rs ❱ ♦r t♦ ♥r② rs

♦♠♥t♦r ♣r♦♠ ❲ s♥ ♥ t♦♥ tt t ♥r♥ ♦r ♠①tr ♠♦ ♥ ♣r♦r♠ ♥ ♠ ♦rt♠ ♦r ♦♥ ♦ ts ①t♥s♦♥s t♠①♠③t♦♥ ♦ t ♦♠♣tt ♦♦♦ s s② ♦r t s ♥♦t t s♦r t ♠ ♠♦ s♥ t st♠t♦♥ ♦ t srt ♣r♠trs ♦ t ♠①♠♠♣♥♥② strt♦♥ s ♦♠♥t♦r ♣r♦♠ ♥ S(a, b) s t ♥♠r♦ ♣♦ss srt♦♥s r♦♠ st ♦ r♥ a ♥t♦ st ♦ r♥ b t♥ δkb s♥ ♥ t srt s♣ ♦ ♠♥s♦♥

∏dkb−1j=1 S(m

kbj ,m

kbj+1 ) ♦ ♥ ①st

♥♠rt♦♥ ♦r st♠t♥ t srt ♣r♠trs s ♥r② ♠♣♦ss ♥ ♦ ♦♥t♥s rs t ♠♥② ♠♦ts ♥♦r ♠♥② rs

①♠♣ ♦♠♥t♦r ♣r♦♠ ♥♦ ② t srt ♣r♠trs ♦ t tr rs ♥ mkb = (5, 4, 3) ♠♣s 51 840 ♣♦ssts ♦r δkb

st♠t♦♥ ♠♣ ♣r♠trs r st♠t ♠ ♦rt♠ ♦♥t ss ♣r♦♠ ♥♦ ② t ♥♥♦♥ ss ♠♠rs♣ t ts ♠ st♣t ♠①♠③t♦♥ ♦ t ①♣tt♦♥ ♦ t ♦♠♣tt ♦♦ s ♥♣♥♥t② ♣r♦r♠ ♦♥ t ♣r♠trs ♦ ♦ s t t ♠ st♣ ♦ trt♦♥[r] t ♦♠♥t♦r ♣r♦♠ ♦ t srt ♣r♠tr st♠t♦♥ ♦r ♦ b ♦♦♠♣♦♥♥t k s ♦r♠ ② tr♦♣♦sst♥s ♦rt♠ ♦s t stt♦♥r②strt♦♥ s ♦s t♦ p(δkb|①kb, ③[r]) ♣r♦♣♦s strt♦♥ ♦ ts ♦rt♠r♥♦♠② s♠♣s t ♥t δ⋆kb t ♥t (ρ⋆kb, ξ

⋆kb, τ

⋆kb) s tr♠♥

st② ♦♠♣t ♥ ♦rr t♦ ♠①♠③ p(ρ⋆kb, ξ⋆kb, τ

⋆kb, δ

⋆kb|①kb, ③[r]) ♦t tt t

♦♥t♥♦s ♣r♠trs (ρ⋆kb, ξ⋆kb, τ

⋆kb) r ♦♥t♦♥② ♦t♥ ② ♥ ♠ ♦rt♠

② ♥tr♦♥ s♦♥ t♥t r ♥♦t♥ t ♠♠rs♣ ♦ t ♣♥♥②strt♦♥s ♦ t ♦ ♥♣♥♥ ♦r ♠①♠♠ ♣♥♥② strt♦♥

Page 91: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s

♦ ♦rt♠

♥ ♥r♥ ♦ ♣r♦r♠ ♥ ♠ ♦rt♠ ♦r♦♠♥t ♣r♦♠ ♦ t ss ♠♠rs♣ ♦r s t ♦♣t♠③t♦♥ ♦ t ①♣tt♦♥ ♦ t ♦♠♣tt ♦♦♦ ♦♥ t srt ♣r♠trs s ♣r♦r♠ st♦st ♦rt♠ ♥ ♦♥② ssr t ♥rs ♦ t ①♣tt♦♥ ♦t ♦♠♣tt ♦♦♦ ♥ ♥♦t ts ♠①♠③t♦♥ ♦ t ♥r♥ s ♣r♦r♠ t ♦♦♥ ♠ ♦rt♠

trt♥ r♦♠ ♥ ♥t θ[0] ts trt♦♥ [r] s rtt♥ s st♣ t ♦♥t♦♥ ♣r♦ts

tik(θ[r]) =

π[r]k p(xi;σk,α

[r]k )

p(xi;σ,θ[r])

.

st♣ ♥rs ♦ t ①♣tt♦♥ ♦ t ♦♠♣tt ♦♦♦

π[r+1]k =

n[r]k

n♥ α

[r+1]kb s s Lkb(α

[r+1]kb ;①, t[r]) ≥ Lkb(α

[r]kb ;①, t

[r]),

r Lkb(αkb;①, t[r]) =

∑ni=1 tik(θ

[r]) ln p(xkbi ;αkb) t t[r] = (tik(θ[r]); i =

1, . . . , n; k = 1, . . . , g) ♥ n[r]k =

∑ni=1 tik(θ

[r])

♦rt♠ ♠ ♦rt♠ t♦ ♦t♥ t ♠ ♠♦ ♠

s ♦rt♠ s st♦♣♣ tr rmax trt♦♥s ♦♣t♠③t♦♥ ♦♥ αkb s♥♣♥♥t② ♣r♦r♠ ♦r (k, b) t t ♠ st♣ ② t ♦♦♥ tr♦♣♦sst♥s ♦rt♠

♦s ♦♥ t st♣ ♦ t ♦rt♠

♥ tr♦♣♦sst♥s ♦rt♠ s ♥♣♥♥t② ①t ♦r (k, b) ♥ ♦rr t♦ ♣r♦r♠ t ♠ st♣ ♦ ♦rt♠ ♦r ① (k, b) ts♦rt♠ s stt♦♥r② strt♦♥ ♦s t♦ p(αkb|①, t[r]) ♥ t s ♣r♦r♠t trt♦♥ [r] ♦ t ♦ ♠ ♦rt♠ t s♠♣s sq♥ ♦ t ♦♣r♠trs (α[r,0]

kb , . . . ,α[r,smax]kb ) r smax s t ♥♠r ♦ trt♦♥s ① ② t

sr s t ♦rt♠ ♠s t ♥♥ t ♠①♠③♥ t ①♣tt♦♥ ♦ t♦♠♣tt ♦♦♦ ♣t

α[r+1]kb = argmax

s=1,...,smax

Lkb(α[r,s]kb ;①, t[r]).

❲ ♥♦ t t tr♦♣♦sst♥s ♦rt♠ t♥ t ts ♥str♠♥t strt♦♥ q(.;α[r,s]

kb )

Page 92: Model-based clustering for categorical and mixed data sets

①♠♠ ♦♦ st♠t♦♥ ♦rt♠

trt♥ r♦♠ t ♥t α[r,0]kb = α

[r]kb ts trt♦♥ [s] s rtt♥ s

α⋆kb ∼ q(αkb;α

[r,s]kb )

α[r,s+1]kb =

α⋆kb t ♣r♦t② λ[r,s]

α[r,s]kb t ♣r♦t② 1− λ[r,s].

♦rt♠ tr♦♣♦sst♥s ♦rt♠

♦s ♦♥ t ♣r♦♣♦s strt♦♥ ♥str♠♥t strt♦♥ q(αkb;α[r,s]kb )

s♠♣s t ♥t α⋆kb ♥ t♦ st♣s rst② t ♥♦r♠② s♠♣s t ♥t

δ⋆kb ♠♦♥ t ♥♦r♦♦ ♦ δ[r,s]kb ♥♦t ② ∆(δ

[r,s]kb ) s ♥♦r♦♦ s

♥ s t st ♦ t ♣r♠trs r t ♠♦st t♦ srt♦♥s r r♥t r♦♠t♦s ♦ δ[r,s]

kb ♦♥② t ♦♠♣ts t ♦♥t♥♦s ♣r♠trs ♦♥t♦♥② ♦♥∆(δ

[r,s]kb ) s s

(ρ⋆kb, ξ⋆kb, τ

⋆kb) = argmax

ρkb,ξkb,τkb

Lkb(ρkb, ξkb, τ kb, δ⋆kb;①, t

[r]).

♦t tt t ♠①♠③t♦♥ ♦ t ①♣tt♦♥ ♦ t ♦♠♣tt ♦♦♦st②s ♥♦t strt♦rr ♥ ♥ t srt ♣r♠trs r ♥♦♥ ♦r② r♠r♥ tt t ♦ strt♦♥ s ts ♠①tr ♥tr♦ s♦♥t♥t r ♥t♥ t ♦ strt♦♥ ♠♠rs♣ ♥♣♥♥ ♦r ♠①♠♠ ♣♥♥② strt♦♥ s t ♦♥t♥♦s ♣r♠trs ♥ ② t♣r♦s qt♦♥ r ♦t♥ ② ♥ ♠ ♦rt♠ t ♥ t ♥①t st♦♥

①♠♣ ♦r♦♦ ♦ t srt ♣r♠tr r stts t♠♥ts ♦ ∆(δkb) t dkb = 2 mkb1 = 3 mkb2 = 2 ♥ t δ121kb = δ221kb =δ322kb = 1 ♥ δh2h

kb = 0 ♦trs

♦s ♦♥ t ♣t♥ ♣r♦t② ♥ ♦rr t♦ ♦♠♣t t ♥t♦♥ ♦t tr♦♣♦sst♥s ♦rt♠ ♣rs t ♣t♥ ♣r♦t② s♥ ②

λ[r,s] = min

p(①kb, t[r];α⋆kb)

p(①kb, t[r];α[r,s]kb )

|∆(δ⋆kb)||∆(δ

[r,s]kb )|

; 1

,

|∆(δ[r,s]kb )| ♥♦t♥ t r♥ ♦ ∆(δ

[r,s]kb )

♠r ①st ♣♣r♦ s st♦st ♦♥ ❲♥ t s♣ ♦ ♣♦ssδkb s s♠ ♦r ①♠♣ ♥ t ♦ r♦♣s s♠ ♥♠r ♦ ♥r② rs♥ ①st ♣♣r♦ ♦t♥s t s♠ rsts s t ♣r♦♣♦s ♦rt♠ t ss♦♠♣tt♦♥ t♠ s t rt♥ ♣♣r♦ ①st ♦r st♦st ♣♥s♦♥ t ♥♠r ♦ rs ♥ ♠♦ts

Page 93: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s

xkb11

xkb12

xkb13

xkb22

xkb21

xkb11

xkb12

xkb13

xkb22

xkb21

xkb11

xkb12

xkb13

xkb22

xkb21

xkb11

xkb12

xkb13

xkb22

xkb21

xkb11

xkb12

xkb13

xkb22

xkb21

r ♦r t r♦ h′ ♥ t ♦♠♥ h ♥ts tt δh2h′

kb = 1♥ t tt δh2h

kb = 0 δkb r t ♠♥ts ♦ ∆(δkb)

tr♠♥t♦♥ ♦ (ρ⋆kb, ξ⋆kb, τ

⋆kb) ② t ♣r♦♣♦s str

t♦♥

s♦♥ t♥t r t rst t♥t t♦r ③ ♥ts t ss ♠♠rs♣ s♦♥ t♥t t♦r ♥♦ts t ♦ strt♦♥ ♠♠rs♣ t s♥♦t ② ② = (y

kbi ; i = 1, . . . , n; k = 1, . . . , g; b = 1, . . . ,k) r ykbi = 1

①kbi rss r♦♠ t ♠①♠♠ ♣♥♥② strt♦♥ ♦r ♦ b ♦ ss k ♥

ykbi = 0 ① kb

i rss r♦♠ t ♥♣♥♥ strt♦♥ ♦r ♦ b ♦ ss k

♦♠♣tt ♦♦♦ ♦ ♠①tr ♠♦ strt♦♥♦rrs♣♦♥s t♦ t ♠r♥ strt♦♥ ♦ t r♥♦♠ r ❳ ♦t♥ r♦♠t tr♣t strt♦♥ ♦ t r♥♦♠ rs (❳,❨,❩) ♥ t ♦s r♥♣♥♥t ♦♥t♦♥② ♦♥ ❩ t ♦♠♣tt ♦♦♦ ♦t ♥ ❨♥ ❩ s ♥ s

L(θ;x,②, ③) =

g∑

k=1

♥k ln πk +g∑

k=1

k∑

b=1

Lkb(αkb;x,②, ③),

Page 94: Model-based clustering for categorical and mixed data sets

①♠♠ ♦♦ st♠t♦♥ ♦rt♠

r Lkb(αkb;x,②, ③) ♥♦ts t ♦♠♣tt ♦♦♦ ♦ ♦ b ♦r♦♠♣♦♥♥t k ♥ ②

Lkb(αkb;x,②, ③) =

n∑

i=1

zik

(

(1−ykbi ) ln

(

(1−ρkb)p(①kbi ; ξkb)

)

+ykbi ln

(

ρkbp(①kbi ; τ kb, δkb)

)

)

.

♦♥t♦♥ st♠t♦♥ ♦ t ♦♥t♥♦s ♣r♠trs t trt♦♥ [s] ♦♦rt♠ ♣r♦r♠ t trt♦♥ [r] ♦ ♦rt♠ t srt ♥t♣r♠tr δ⋆kb s s♠♣ ♥ t ♦♥t♥♦s ♥t ♣r♠trs r ♥s ♦♦s

(ρ⋆kb, ξ⋆kb, τ

⋆kb) = argmax

ρkb,ξkb,τkb

Lkb(ρkb, ξkb, τ kb, δ⋆kb;①, t

[r]).

♦ ♦♥t♦♥② ♦♥ (δ⋆kb,①, t[r]) t ♦♥t♥♦s ♣r♠trs r ♦t♥ ② t

♦♦♥ ♠ ♦rt♠

trt♥ r♦♠ ♥ ♥t (ρ[0]kb ,α

[0]kb , τ

[0]kb) trt♦♥ [ℓ] s rtt♥ s

st♣ t t ♦♥t♦♥ ①♣tt♦♥ ♦ ykbi

ui(α[ℓ]kb) =

ρ[ℓ]kbp(①

kbi ; τ

[ℓ]kb, δ

⋆kb)

(1− ρ[ℓ]kb)p(①

kbi ; ξ

[ℓ]kb) + ρ

[ℓ]kbp(①

kbi ; τ

[ℓ]kb, δ

⋆kb),

st♣ ♠①♠③t♦♥ ♦ t ①♣tt♦♥ ♦ t ♦♠♣tt ♦♦♦

ρ[ℓ+1]kb =

n[ℓ]kb

n[r]k

, ξjh[ℓ+1]kb =

njh[ℓ]kb

n[r]k − n

[ℓ]kb

♥ τh[ℓ+1]kb =

nh[ℓ]kb

n[ℓ]kb

,

t n[ℓ]kb =

∑ni=1 tik(θ

[r])ui(α[ℓ]kb) n

h[ℓ]kb =

∑ni=1 tik(θ

[r])ui(α[ℓ]kb)x

kb1hi

♥ njh[ℓ]kb =∑n

i=1 tik(θ[r])(1− ui(α

[ℓ]kb))x

kbjhi .

♦rt♠ ♠ ♦rt♠ t♦ ♦t♥ (ρ⋆kb, ξ⋆kb, τ

⋆kb)

♦♥tr ♥ ♦♣t♠♠ r♥ ♦r ①♣r♠♥ts ♠♣r② ♥♦t tt t ♦♦♦ ♥t♦♥ ♦ t ♠①tr t♥ t ♥♣♥♥ ♥ t♠①♠♠ ♣♥♥② strt♦♥s ♥q ♦♣t♠♠ ❲ ♦♥tr tt ts♥t♦♥ s ♥ ♥q ♠①♠♠

♠r ♥ ♠ ♦rt♠ t srt ♣r♠trs r ♥♦♥ ♥ ts♣ s r δkb r ♥♦♥ ♦r (k, b) t ♦♥t♥♦s ♣r♠trs ♦ st♠t ♥q ♠ ♦rt♠ t trt♦♥ [r] ♦ ts ♦rt♠ t st♣♦ ♦♠♣t ♦t ①♣tt♦♥s ♦ ③[r] ♥ ②[r] t ♠ st♣ ♦ st♠t t ♦♥t♥♦s ♣r♠trs ♠①♠③♥ t ①♣tt♦♥ ♦ t ♦♠♣tt♦♦♦

Page 95: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s

♦ st♦♥ ♦rt♠

♠ ♠ s t♦ st t ♠♦ ♥ ② (g, σ) ttr t t ts ♥ ②s♥ r♠♦r t ♠ s t♦ ♥ t ♠♦ ♥ t rst♣♦str♦r ♣r♦t②

Pr♦r strt♦♥s ❲ ♦♥sr tt p(g) = 1gmax

g ≤ gmax ♥ 0 ♦trsr gmax s t ♠①♠♠ ♥♠r ♦ sss ♦ ② t sr ♥ ss♠tt p(σ|g) ♦♦s ♥♦r♠ strt♦♥

P♦str♦r strt♦♥s st ♠♦ ♠①♠③s ts ♣♦str♦r strt♦♥♦r♥ t♦ t ♣r♦r strt♦♥s t s ♥ s

(g, σ) = r♠①g

[

r♠①σ

p(①|g,σ)]

r p(①|g,σ) ∝∫

θ∈Θ

p(①|θ, g,σ)p(θ)dθ.

♦ ♥ (g, σ) ♠♠ ♦rt♠ s s ♦r st♠t♥ r♠①σ p(①|g,σ) ♦r ♦ g ∈ 1, . . . , gmax s ♠t♦ ♠ts t ♦♠♥t♦r ♣r♦♠♥♦ ② t tt♦♥ ♦ t ♦ strtr ♦ rs s♥ t ♣r♦s r♥♦♠ ♠♦♥ t σ ♦ ♥trst

♠r ♥ t rrs ♠♣ rrs ♠♣ ♠t♦ ♦ s❬❪ ♦r ts ♣♣r♦ s rr② ♣r♦r♠ t ♠① ♣r♠trs ♦♥t♥♦s ♥ srt ♥ ♥ s s t s t t♦ ♥ ♠♣♣♥t♥ t ♣r♠trs s♣ ♦ t♦ ♠♦s ♦ ♣r♦♣♦s t♦ s ♥ sr ♠♠♦rt♠ ♥ p(σ|①, g) s stt♦♥r② strt♦♥

①♣♦rt♦♥ ♦ t s♣ ♦ t ♠♦s ②

♦rt♠

♥ s ♦rt♠ tr♥ts t♥ t♦ st♣s t ♥rt♦♥ ♦ ♥♦r♦♦ ♦♥t♦♥② ♦♥ t rr♥t ♠♦ ② ♣r♦♣♦s strt♦♥ ♥ t ♥rt♦♥ ♦ ♥ ♠♦ ♦♥♥ t♦ ts ♥♦r♦♦ ♦r♥ t♦ ts ♣♦str♦r♣r♦t②

Page 96: Model-based clustering for categorical and mixed data sets

♦ st♦♥ ♦rt♠

s ♠♠ ♦rt♠ s p(σ|①, g) s stt♦♥r② strt♦♥ trt♥r♦♠ ♥ ♥t ♦ t r♣rtt♦♥ ♦ t rs ♥t♦ ♦s σ[0] tstrt♦♥ [q] s rtt♥ s

♦r♦♦ st♣ s♠♣♥ ♦ st♦st ♥♦r♦♦ Σ[q]

Σ[q] ∼ q(Σ;σ[q]).

♦ st♣ s♠♣♥ ♦ t r♣rtt♦♥ ♦ t rs ♥t♦ ♦sσ[q+1]

σ[q+1] = p(σ|①, g,Σ[q]) r p(σ|①, g,Σ[q]) ∝

p(①|g,σ) σ ∈ Σ[q]

0 ♦trs

♦rt♠ ♦rt♠ t♦ ①♣♦r t ♠♦s

❲ ♥♦ t ♦t st♣s ♦ t ♦ ♠♠ ♦rt♠

ts ♦ t ♦r♦♦ st♣ tr♠♥st ♥♦r♦♦ ♦ σ[q] ♦ ♥ s t st ♦ ♠♦s r t ♠♦st ♦♥ r s t ♦r ♦♥♦♠♣♦♥♥t ♥ ♥♦tr ♦ ♣♦sst② t♦ ♥ ♦

σ : ∃!(k, b, j) j ∈ σ[q]kb ♥ j /∈ σkb

σ[q]

.

♦r s ts tr♠♥st ♥♦r♦♦ ♥ r② r ♦r ♣r♦♣♦sstrt♦♥ ♦s r♥ t t♦ st♦st ♥♦r♦♦ Σ[q] ② ♠t♥ t♥♠r ♦ (k, b) r σkb ♦ r♥t t♦ σ

[q]kb s t s♠♣♥ ♦r♥

t♦ q(.;σ[q]) s ♣r♦r♠ ② t tr ♦♦♥ st♣s ♦♠♣♦♥♥t s♠♣♥

k[q] ∼ U [1, . . . , g].

♥ ♦ s♠♣♥

b[q]from ∼ U [1, . . . , B[q]

k[q]].

rr♥ ♦ s♠♣♥

b[q]to = b[q], B[q]

k[q]+ 1 r b[q] ∼ U [1, . . . , B[q]

k[q] \ b[q]from].

st♦st ♥♦r♦♦ Σ[q] s t♥ ♥ s

Σ[q] =

σ : ∃!(k, b, j) j ∈ σ[q]kb , j /∈ σkb ♥ j ∈ σkb′ t k = k[q], b = b

[q]r♦♠, b

′ ∈ b[q]t♦

σ[q]

.

❲ ♥♦t ② σ[q+ε(e)] t ♠♥ts ♦ Σ[q] r ε(e) = e

|Σ[q]|+1♥ e = 1, . . . , |Σ[q]|

①♠♣ ♥♦r♦♦ Σ[q] r s♦s ♥ strt♦♥ ♦ ts ♥

t♦♥ ♦ t ♥♦r♦♦ Σ[q] ♥ σ[q]k = (1, 2, 3, 4)

Page 97: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s

X4

X3

X2

X1

X1

X2

X3

X4

X4

X3

X2

X1

X1

X2

X3

X4

X4

X3

X2

X1

X1

X2

X3

X4

X4

X3

X2

X1

X1

X2

X3

X4

X4

X3

X2

X1

X1

X2

X3

X4

X4

X3

X2

X1

X1

X2

X3

X4

X4

X3

X2

X1

X1

X2

X3

X4

X4

X3

X2

X1

X1

X2

X3

X4

r ①♠♣ ♦ t s♣♣♦rt ♦ Σ[q] ♥ t s ♦ ♦r rs trs ♦ t jt r♦ ♥ ♦ t j

′t ♦♠♥ r ♥ t s♠ ♦ t♥ t (j, j′) s ♣♥t ♥ s s ♣♥t ♥ t ♦trs ♠♥ts♦ Σ[q] b[q]from = 1 ♠♥ts ♦ Σ[q] b[q]from = 2

ts ♦ t Pttr♥ st♣ t t ♥rt♦♥ ♣ttr♥ st♣ t ♦rt♠ ♥st ♦ p(①|g,σ) ∀σ ∈ Σ[q] t♦ ♠♣♠♥t ♦rt♠ ② s♥ t ♣♣r♦①♠t♦♥ ts ♣r♦t② s ♣♣r♦①♠t ②

ln p(①|g,σ) ≃ L(θ;①, g,σ)− ν♠2

log(n),

θ ♥ t ♠①♠♠ ♦♦ st♠t♦r ♦t♥ ② t ♠ ♦rt♠ ♣r♦s② sr ♥ t♦♥ s t trt♦♥ [q] ♦r e = 1, . . . , |Σ[q]| tst♠t♦r θ

[q+ε(e)]ss♦t t♦ t ♠♥t σ[q+ε(e)] s ♦♠♣t ② ♦rt♠

♥t③t♦♥ ❲tr t ♥t st ♦r σ[0] t ♦rt♠ ♦♥rst♦ t s♠ stt♦♥r② strt♦♥ ♦r ts ♦♥r♥ ♥ r② s♦♥ t ♥t③t♦♥ s ♣♦♦r ♥ ♦s ♦♥sst ♥ t ♠♦st ♦rrt rs rr s♥♥t sst♦♥ s ♣♣ ♦♥ t ♠tr① ♦ r♠rs❱ st♥s ♦♥ t ♦♣s ♦ rs ❲ st ♦r σ[0]

k t ♣rtt♦♥ ♣r♦② t ♠♥♠③s t ♥♠r ♦ ♦s ♥ ①s t ♦s♦♥sst♥ ♦ ♠♦r t♥ ♦r rs ♦t tt t ♥♠r ♦ t rs t♥t♦ ♦ s ♠t t♦ ♦r ♦r t ♥t③t♦♥ s r② ♦s ♥♠♦r t♥ ♦r rs r ♦sr r♥ ♦r ①♣r♠♥ts ♦s② t♠♠ ♦rt♠ ♥ t♥ ♦t ts ♥t ♦♥str♥t ♥ssr②

t♦♣♣♥ rtr♦♥ ♦rt♠ s st♦♣♣ ♥ qmax sss trt♦♥s ♥♦t s♦r ttr ♠♦

Page 98: Model-based clustering for categorical and mixed data sets

♠r ①♣r♠♥ts ♦♥ s♠t t sts

♦♥sq♥s ♦ t ♠♦ st♦♥ ♦♥ t ♦

rt♠

♥ t trt♦♥ [q] ♦ t ♠♠ ♦rt♠ ♣r♦r♠♥ t ♠♦ st♦♥

♦rt♠ t ♠ ♦rt♠ ♦rt♠ st♠ts θ[q+ε(e)]

ss♦t t♦ t ♠♦ σ[q+ε(e)] ♦r e = 1, . . . , |Σ[q]| ♥ ts ♠♦s r ♦s t♦

σ[q] tr ♠①♠♠ ♦♦ st♠ts s♦ ♦s t♦ θ[q]

Pr♠trs ♦ t ♥♦♥♠♦ ♦s ♠ ♦rt♠ ♥t③t♦♥ s

s♦ ♦♥ ② t ♦ θ[q]

♦r t ♥♦♥ ♠♦ ♦s s ♥ s s

σ[q+ε(e)]kb = σ

[q]kb θ

[q+ε(e)][0]kb = θ

[q]

kb

Pr♠trs ♦ t ♠♦ ♦s ♦r t ♦tr ♦s t ♦♥t♥♦s ♣r♠trs r r♥♦♠② s♠♣ ♥ ♦rr t♦ ♦ t ♦♠♥t♦r ♣r♦♠s s sq♥t ♠t♦ t♦ ♥t③ δ[q+ε(e)][0]

kb srt♦♥s r♦♠ xkb1i t♦ x

kbji

r s♠♣ ♦r♥ t♦ ① ♥ t ♦♥t♥♦s ♣r♠trs ♣r♦s② s♠♣(ρ

[q+ε(e)][0]kb ,α

[q+ε(e)][0]kb , τ

[q+ε(e)][0]kb ) ♦r j = 2, . . . , dkb s ♦♦s

δ.j[q+ε(e)][0]kb ∝

n∏

i=1

p(xkb1i , x

kbji ; ρ

[q+ε(e)][0]kb ,α

1[q+ε(e)][0]kb ,α

j[q+ε(e)][0]kb , τ

[q+ε(e)][0]kb , δ.jkb)

z[q]ik ,

r δ

.j[q+ε(e)]kb = (δ

hj[q+ε(e)]kb ;h = 1, . . . ,m

kb1 ) ♥ r z[q]ik = E

[

Zik|xi,θ[q]]

♠r ♦t t ♥♠r ♦ trt♦♥s ♦ t ♦rt♠ rmax ss ♥ t♦♥ t ♦rt♠ s st♦♣♣ tr ① ♥♠r ♦ trt♦♥srmax t ♦rt♠ s st♦♣♣ ♦r ts ♦♥r♥ t ♣r♦♣♦s ♥t③t♦♥♠ts t ♣r♦♠s ♥ t ♠♦ s ♣♦str♦r ♣r♦t② t st② ♥ t ♥♦r♦♦ Σ[q] r♥ s♦♠ sss trt♦♥s s♦ ts ♦♦♦ ♥rs s ts ♦rt♠s r ♥tr♦ t ♥♠r ♦ trt♦♥s ♦ ♦rt♠ t ♠♦st ♥tr♥ ♦rt♠ s s♠ ❲♥ t st ♠♦ s st② ♦rt♠ ts ttr st② ♥ ts ♠♦ r♥ ♠♥② trt♦♥s s♦ ttr♦♣♦sst♥s ♦rt♠ ♥ t ♠ ♦rt♠ ♦rt♠ r♣r♦r♠ ♦ts ♦ t♠s s t s ♥♦t ♥ssr② t♦ r ♥♠r ♦ trt♦♥ss st♦♣♣♥ rtr♦♥

♠r ①♣r♠♥ts ♦♥ s♠t t sts

♣rs♥ts t st♠♥t ♣r♠trs s s ♦r t s♠t♦♥s

♦rt♠s ♠♠ ♠ tr♦♣♦sst♥s ♠

rtr qmax = 20× d rmax = 10 smax = 1 tmax = 5

❱s ♦ t r♥t st♦♣♣♥ rtr

Page 99: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s

t② ♦ t ♦rt♠ ♦r t δkb st♠t♦♥

♠ ♥ ts st♦♥ strt t ♣r♦r♠♥ ♦ t tr♦♣♦sst♥s ♦rt♠ st♠t♥ δkb s t♦♥ ♥ t r♥ ♦ ts ♥t③t♦♥ ♥② ♥ ts ♦rt♠ s ♥tr♦ ♥ t ♠♠ ♦rt♠ ♥ ♥ t ♠♦rt♠ rs♣t② st♠t t ♠♦ ♥ t ♣r♠trs ♥ t t♦♦♥r q② t s s♦♥ ♥ t ♦♦♥ s♠t♦♥s tt t ♦rt♠ st②sr♥t ♣ t♦ s① ♠♦ts ♣r r ♥ ♣ t♦ s① rs ♣r ♦ s♦♥t♦♥s ♦ ♥ ♠♦st stt♦♥s

①♣r♠♥t ♦♥t♦♥s ♠♣s ♦ s③ sr ② rs ♥ ts♠ ♥♠r ♦ ♠♦ts r ♥rt ② ♠①tr t♥ ♥ ♥♣♥♥strt♦♥ ♥ ♠①♠♠ ♣♥♥② strt♦♥ ♣r♠trs r s♦ st♠t ② t tr♦♣♦sst♥s ♦rt♠ sr ♥ t♦♥ s♥ ♦♥②♦♥ ss s ♥rt ♥t③t♦♥s ♦ t srt ♣r♠trs r ♣r♦r♠♦r♥ t♦ qt♦♥ t zi1 = 1 ♦r i = 1, . . . , 200

sts r s♦s t ♦①♣♦ts ♦ t ♥♠rs ♦ trt♦♥s rqr ②t tr♦♣♦sst♥s ♦rt♠ ♥ ♦rr t♦ ♥ t tr ♥s t♥ ♠♦ts ♠①♠③♥ t ♦♦ ♦r♥ t♦ ts s♠t♦♥s ♦♥ ♦srs ttt rsts ♦ ts ♦rt♠ r ♦♦ t♥s t♦ ts ♥t③t♦♥ ♦s s♥♥t② r♥ t ♥♠r ♦ trt♦♥s ♥ ♥ ♦rr t♦ ♥ t ♠①♠♠♦♦ st♠t♦rs

t② ♦ t ♦rt♠ ♦r ♠♦ st♦♥

♠ ♥ ♦rr t♦ strt t ♥② ♦ t ♦rt♠ ♦r t ♠♦ st♦♥♥ s♦ t ♥ st♠t♦♥ ♣r♦ss ♥t t♦ st② t ♦t♦♥ ♦ tr r♥ ♦r♥ t♦ t ♥♠r ♦ rs ♥ t♦ t s③ ♦t t st

①♣r♠♥t ♦♥t♦♥s ♥ ♠♥② stt♦♥s s♠♣s r ♥rt ♦r♥ t♦ t ♠ ♠♦ t t♦ ♦♠♣♦♥♥ts ♦t tt t ♣r♠tr u s♥tr♦ ♦r ♦♥tr♦♥ t ♦r♣♣♥ ♦ sss ♥ t s ♦s t♦ ♦♥ t♥t sss r s♦t② ♦r♣♣ s ♣r♠tr ① t rr♦r rt t♦ ♦r st stt♦♥

σkb = (d/b, 1 + d/b) ρkb = 0.6(1− u) τ kb = (0.60, 0.20, 0.20),

δh2h′

1b = 1 h = h′ δ1221b = δ2231b = δ3211b = 1 αj1b = (0.20, 0.20, 0.60),

α12b = α1

1b(1− u)+ (0.075, 0.850, 0.075)u ♥ α22b = α2

1b(1− u)+ (0.850, 0.075, 0.075)u.

♥ t t ♦rt♠ s st♦♣♣ s s♦♦♥ s t ♥s srt st♠t ♥♦♥ ♦♦r t♥ ♦r q t♦ t ♦♦ ♦t♥ t t tr srt ♣r♠trs s ♦r ts♠t♦♥

Page 100: Model-based clustering for categorical and mixed data sets

♠r ①♣r♠♥ts ♦♥ s♠t t sts

r ♦①♣♦ts ♦ t ♥♠r ♦ trt♦♥s rqr ② t tr♦♣♦sst♥s ♦

rt♠ ♥ ♦rr t♦ ♥ t st ♥s t♥ ♠♦ts ♦r♥ t♦ t ♥♠r ♦ ♠♦ts

♥ tsts r s♠t t ♣r♦♣♦rt♦♥ ♦ ♠①♠♠ ♣♥♥② strt♦♥ q t♦

r rs t t ♣r♦♣♦s ♥t③t♦♥ r ♠♦ts ♣r rs t t

♣r♦♣♦s ♥t③t♦♥ r rs t r♥♦♠ ♥t③t♦♥ r ♠♦ts ♣r

rs t r♥♦♠ ♥t③t♦♥

sts s♦s t ♠♥ ♥ t st♥r t♦♥ ♦ t r r♥ t♥ t ♣r♠trs s ♦r t t st ♥rt♦♥ ♥ tst♠t ♣r♠trs ♦r♥ t♦ t ♥♠r ♦ rs ❲♥ n ♥rss tr r♥ ♦♥rs t♦ ③r♦ t ♦♥r♠s t ♦♦ ♦r ♦ t♣r♦♣♦s ♦rt♠

Page 101: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s

d \ n

♠♥ st♥r t♦♥ ♦ t r r♥

♥②ss ♦ t♦ r t sts

♦♥tr♣t ♠t♦ ♦

t s t st s sst ♦ t t♦♥ ♥♦♥s ♦♥tr♣tPr♥ r② ❬❪ t s ♦♠♣♦s t ♠rr ♦♠♥ ♦ rtr ♥♦t ♣r♥♥t ♦r ♦ ♥♦t ♥♦ t② r t t t♠ ♦ ♥tr ♦r♥♣r♦♠ s t♦ ♣rt t rr♥t ♦♥tr♣t ♠t♦ ♦ ♥♦ s ♦♥tr♠♠t♦s ♦r s♦rt tr♠ ♠t♦s ♦ ♦♠♥ s ♦♥ r ♠♦r♣ ♥ s♦♦♦♥♦♠ rtrsts ♦♠♥ s sr ② ♥♥ rs ♥♠r ♦r♥ r ♦r♥ ♥ ♠♦r s ❲ ♥ ss ♥ ♠♦r s t♦♥ ❲ ♦ s♥st♦♥ ♦ s♥s ♦♣t♦♥ st♥r ♦ ♥ ♥① ♦ s r♦♥ ❲ ♦♥s♠♦r s♠ s ♥♦ ♦r♥ ❲❲♦ ②s ♦r ♥♦ ♥ ♠ ①♣♦sr ♦♦ ♦r♥♦t ♦♦ ♦r t ♥②ss t ♦♥tr♣t ♠t♦ s s ♥ ♥ ♦rr t♦♦r ♥ str♥ ♦♥t①t

♦ st♦♥ ♣rs♥ts t s ♦ t rtr♦♥ ♦r t ♠♥ t ♠ ♠♦s ❯♥t ♦r sss t rsts ♦ t ♠ ♠♦ r ttrt♥ t♠ ♦ t ♠ ♠♦ st♦♥ ♦ ss ♥♠r s ttr ♦r t ♠♠♦ s♥ t sts t tr ♥♠r ♦ sss t ♠ ♠♦ ♦rst♠tst

g ♠ ♠

❱s ♦ t rtr♦♥ ♦t♥ ② ♦t ♠♦s t r♥t♥♠rs ♦ sss st s ♦r♥ t♦ t rtr♦♥ r ♥ ♦

♦ ♥tr♣rtt♦♥ r s♠♠r③ t rsts ♦ t st♠♠♦♦r♥ t♦ t rtr♦♥ t ♦s t♦ sr t sss ② tr ♠♥ trs ♣r♦♣♦rt♦♥s ♥trss ♦rrt♦♥s ♥ ♦r♥ts t st♠t sss rr♣rs♥t t rs♣t t♦ tr ♣r♦♣♦rt♦♥ ♥ rs♥ ♦rr ♦t tt tr♦rrs♣♦♥♥ r ♣♥s ♦♥ tr ♣r♦♣♦rt♦♥ ♠t ♣r♦♣♦rt♦♥s r

Page 102: Model-based clustering for categorical and mixed data sets

♥②ss ♦ t♦ r t sts

♥t ♦♥ t t s ♥ sss tr ♥t♦♥s r ♥ rst ♦♥ st ♥trrs ♦rrt♦♥s ρkb ♦r t ♦s ♦ t ss ♦rr ② trstr♥t ♦ ♦rrt♦♥ ♥ rs♥ ♦rr s♦♥ ♦♥ s t ♥trrs♦rrt♦♥s τ kb ♦r ♦ r♥ ♦r♥ t♦ t str♥t ♦ tr ♣♥♥s ♥ rs♥ ♦rr tr s t rs r♣rtt♦♥ ♣r ♦s ♥ts tt t r s ss♥ t♦ t ♦ ♥ t ♥tstt ♦♥t♦♥② ♦♥ ts ss t r s ♥♣♥♥t ♦ t rs ♦ ts♦ ♦r ①♠♣ ts r s♦s tt t rst ss s ♣r♦♣♦rt♦♥ ♦ 0.49♥ tt t rs r s♣t ♥t♦ tr ♦s

ss ②♦♥ ♠s ♥r ts ss ♣r♦♣♦rt♦♥ s q t♦ r r t♦ ♣♥♥②

♦s ♥ ♦♥ ♦ ♦ ♥♣♥♥ ♦ ♥ ts ss t ♦♠♥ ♥ tr r♥ ♥♠r r

♦rrt ρkb t ♣rs♥ ♦ ♦t ①tr♠ stt♦♥s ②♦♥ ♦♠♥t♦t ♥ ♦ ♦♠♥ t ♦ts ♦ r♥ ①♣♥ ② ♦t δkb♥ τ kb

♦ t t♦♥ ♦ ♦t ♠♠rs ♦ ♦♣ r ♦s δkb♥ t♦♥ s ♠♦st ♣rs♥t τ kb

♦ t ♣rt ♦ s♠ s ♥r ♦♣ ♠♠rs ♦s♥ t♦r② t♦ ♥ tr ♥ tr ♥ ♥① st②s ♦ αkb

ss ♦ ♥ ♥♦t ♣rt♥ s♠ ♥r ts ss ♣r♦♣♦rt♦♥ s q t♦ r r t♦ ♣♥♥②

♦s ♥ ♦♥ ♦ ♦ ♥♣♥♥ ♦ tr s str♦♥ ♦rrt♦♥ t♥ t ♥ ♦ t s♥s

♦♣t♦♥ ♥ t s r♦♥ ρkb ♥ ts ss t ♦♠♥ ♣rt♥s♠ ♥r② s♥ t t ♦♣t♦♥s δkb ♥ τ kb

♦ ts ♦ s♦s ♥ t♥ t ♥♠r ♦ r♥ ♥ t ♦ t ♦♠♥ ♦r r t ♦♠♥ t ♠♦r r♥ t② δkb

♦ ♥ ts ss ♦t ♠♠rs ♦ t ♦♣ ♦♥ stsαkb

ss ♣♦♦r ♥ r ♠s ♥r ts ss ♣r♦♣♦rt♦♥ s q t♦ r s ♦♥ ♦ ♦

♥♣♥♥ ♦ ts s ss r t ♥♠r ♦ r♥ s r② % ♦

♦♠♥ t st r♥ t ♦♥ssts ♠♦st② ♦ rtr ♦ ♦♠♥t ♦ s ♦ t♦♥ s s tr s♥s ② ♦r ♥r♦♣s ♥ ♣rt ♦ s♠ s ♥r ♦♥ ♥ ts t♦r② ♥s ♥♦t ①♣♦s t♦ t ♠ αkb

♦♥s♦♥ t s ♥♦t tt t ♠ ♠♦ s ♠♦r r♥t ♦r ts t st♥ t ♥♠r ♦ sss s ♠t ♥ t② r ♥tr♣rt ♥ t♦♥ tss♠♣t♦♥ ♦ ♦♥t♦♥ ♥♣♥♥ t♥ rs s♠s t♦♦ str♥♥t ♦rs♦♠ ♦♣s ♦ r rt♦♥s♣ t♥ ♥ ♥♠r ♦ r♥ rt♦♥st♥ t t♦♥ ♦ ♦t ♠♠rs ♦ ♦♣ ♥ ♦♥tr② r sts②st♠ s ♣rs♥t

Page 103: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s

1 0.75 0.5 0.25 00 0.25 0.5 0.75 1

ρkb τkb σkb

0

0.49

0.86

1

Class 1

Chi

WAg

WEd

HEd

HOc

Liv

WRe

WWo

Med

Chi

WAg

WEd

HEd

HOc

Liv

WRe

WWo

Med

Chi

WAg

WEd

HEd

HOc

Liv

WRe

WWo

Med

Class 2

Chi

WAg

WEd

HEd

HOc

Liv

WRe

WWo

Med

Chi

WAg

WEd

HEd

HOc

Liv

WRe

WWo

Med

Chi

WAg

WEd

HEd

HOc

Liv

WRe

WWo

Med

Class 3

Chi

WAg

WEd

HEd

HOc

Liv

WRe

WWo

Med

r ♠♠r② ♦ t st ♠ ♠♦ ♦r♥ t♦ t rtr♦♥ ♦rt ♦♥tr♣t ♠t♦ ♦ t st

s str♥

t ♥s s♦♥ ♦♠♣♥② s ♦t ♥♦r♠t♦♥ r♦♠ tr♥ rrs ♥ ♦rr t♦ str s st s r sr② ♥♥ rs ♦ ♦r ♣tt ♦r s♥ ♣t ♦r ♦ t ♠♦trst ♦r t ♥ s♦ ♥ t rt trt♠♥t ♥st ♦♠♣t rs♣rt♦r② ss ♥ rr ♠s s♥t♦♥ s ♠s♠♣t②♥ ♠♣ ♠♦tr ♣r♥t trt♠♥t ♥st rs♣rt♦r② ss ♥rr

♥♦r♠t♦♥ rtr s♣②s t rtr♦♥ s ♥ t ♥♠r♦ ♣r♠trs rqr ② t ♠ ♥ t ♠ ♠♦s rtr♠♦r t ♦♠♣t♥ t♠ ♥ ♠♥ts ♦t♥ t ♣r♦ss♦r ♥t ♦r t♦ st♠tt ♠ ♠♦ ② strt♥ ♠♠ ♥s t st♦♣♣♥ rtr♦♥ ♦ qmax = 180 t ♠ ♠♦ ♥s s t t ♣ ①♠♦ ❬+❪

♦r t ♠ ♠♦ t rtr♦♥ sts ♥♠r ♦ sss s♥ tst t sss ♥tr♣rtt♦♥ ♦ t strs s s♦ t ♥ ♥ ss♠ tt t qt② ♦ t st♠t s r② ♣♦♦r r ♣s t♥tr♣rtt♦♥ ♦r t ♠ ♠♦ t ♦♠♣♦♥♥ts st ♠♦ ♦r♥ t♦t rtr♦♥ ts ♥tr♣rtt♦♥ s t s♠ s t ♥tr♣rtt♦♥ ♦ r ♦r ①♠♣ ts r s♦s tt t rst ss s ♣r♦♣♦rt♦♥ ♦ 0.29 ♥ t s♦♠♣♦s ♦ ♦r ♦s ♠♦st ♦rrt ♦ ♦ t rst ss s ρkb ≃ 0.80

Page 104: Model-based clustering for categorical and mixed data sets

♥②ss ♦ t♦ r t sts

g ♠

ν♠

♠ ν♠

t♠ ♠♥

sts ♦r t ♠ ♥ t ♠ ♠♦s ♦r♥ t♦ r♥t ♥♠rs♦ sss ♦r ♦t ♠♦s rst r♦ ♦rrs♣♦♥s t♦ t rtr♦♥ s ♥ ts♦♥ r♦ ♥ts t ♥♠r ♦ ♦♥t♥♦s ♣r♠trs st rsts ♦r♥t♦ t rtr♦♥ r ♥ ♦ ♦♠♣t♥ t♠ ♦r t ♠ ♠♦ st♠t♦♥ s♥ ♥ ♠♥ts

♥ t str♥t ♦ t st ♠♦ts ♥ s ♦s t♦ 0.85 s ♦ ♦♥ssts♥ t rs ♥

1 0.75 0.5 0.25 00 0.25 0.5 0.75 1

ρkb τkb σkb

0

0.29

0.56

0.76

0.9

1

Class 1

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

Class 2

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

Class 3

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

Class 4

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

Class 5

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

Apt

TOC

TRC

TDC

Iso

Dis

Emp

TRM

TDM

r ♠♠r② ♦ t st ♠ ♠♦ ♦r♥ t♦ rtr♦♥ ♦r ts t st

♥tr♣rtt♦♥ ♦ ss r s ♥♦ ♣♦ss ♥tr♣rtt♦♥ ♦ ss ♥♦ttt t ♦trs sss r s♦ ♠♥♥ s ts ♥ ❬❱❪

♥r ts ss s ♣r♦♣♦rt♦♥ q t♦ ♥ ♦♥ssts ♦ tr♦s ♦ ♣♥♥② ♥ ♦♥ ♦ ♦ ♥♣♥♥

♦ tr s str♦♥ ♦rrt♦♥ ρ11 t♥ t rs rrtrt♠♥t ♦ t ♥ ♠♦tr ♣r♥t trt♠♥t ♥st rs♣rt♦r②

Page 105: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦s ♦ ①tr♠ strt♦♥s

ss s♣② t♥ t ♠♦t② ♥♦ trt♠♥t ♥st t rr ♥ t s♥ ♦ ♣r♥t trt♠♥t ♥st rs♣rt♦r② ss ♦ts ♠♦tr τ 11 ♥ δ11

♦ tr s str♦♥ ♦rrt♦♥ ρ12 t♥ t rs trt♠♥t ♥st rs♣rt♦r② ♥ss ♦ t ♥ ♠♦tr ♣r♥t trt♠♥t ♥st rr s♣② t♥ t ♠♦t② ♣r♥t trt♠♥t♥st rs♣rt♦r② ♥ss ♦ t ♥ t ♣rs♥ ♦ rr ♣r♥ttrt♠♥t ♦ ts ♠♦tr τ 12 ♥ δ12

♦ tr ①sts ♥♦tr str♦♥ ♥ t♥ t ♦r ♦ t♠♦tr t ♠♣t②♥ ♦ t ♠ ♥ ts s♥t♦♥ τ 13 ♥ δ13

♦ ts ♦ s rtr③ ② ♥ s♥ ♦ ♣r♥t trt♠♥t♥st ♦♠♣t ♥ ♦♥t♥s 50% ♦ t s ♥t ② ts ♥ss α14

♦♥s♦♥

② s♥ t ♦ ①t♥s♦♥ ♦ t ♠ ♠♦ ♥ ♠①tr ♠♦ t♠ ♠♦ s ♥ ♣r♦♣♦s t♦ str t♦r t ② t♥ ♥t♦ ♦♥tt ♥trss ♣♥♥② ♦ strt♦♥ ♦ t ♠ ♠♦ s ♥ s ♠①tr t♥ ♥ ♥♣♥♥ strt♦♥ ♥ ♠①♠♠ ♣♥♥② strt♦♥ s s♣ strt♦♥ st②s ♣rs♠♦♥♦s ♥ ♦s r♥t s ♦♥tr♣rtt♦♥ rst s ♥ ② t ♦s ♦ rs r♥ ♦t t♦♥t♦♥ ♣♥♥s t♥ rs ♥ ② t ♣r♦♣♦rt♦♥s ♦ t ♠①♠♠♣♥♥② strt♦♥s rtr③ t str♥t ♦ ts ♣♥♥s s♦♥ s ♠♦r ♣rs s♥ t ♣r♠trs ♦ t ♦ strt♦♥ rt t♥s t♥ ♠♦ts ♥ tr str♥ts ♠ ♠♦ s ♥ ♦♠♣rt♦ t t♥t ss ♠♦ ♦♥ t♦ r t sts

♣r♠tr ♥ t ♠♦ r s♠t♥♦s② st♠t ♠♠ ♦rt♠ s ♦rt♠ ♦s t♦ r t ♦♠♥t♦r ♣r♦♠s ♦ t ♦strtr tt♦♥ ♥ t ♥s t♥ ♠♦ts sr ♦r t st♠t♦♥ ♦t ♠①♠♠ ♣♥♥② strt♦♥ rsts r ♦♦ ♥ t ♥♠r ♦♠♦ts s s♠ ♦r r ♦r ♠♦r t♥ s① ♠♦ts t tt♦♥♦ ♦tr ♥s ♠ts s♦♠ ♣rsst♥t ts ♦ t ♦rt♠ ♥ s♦ ♥ts s ♣r♦♣♦s ♣♣r♦ t♦ st♠t t ♦ strtr s ♥♦t ♣t ♦rt sts t ♦ts ♦ rs

♠♥ r ♦ ts ♦rt♠ s ts ♥ t♦ ♦♠♣t t ♠ ss♦tt♦ ♥t ♠♦ s st♠t♦♥ s t♠ ♦♥s♠♥ ♥ ♦♥② t ♠

ss♦t t♦ t st ♠♦ s ♥tr♣rt s ♣r♦♣♦s ♥ t ♥①t ♣tr ♥ ♠①tr ♠♦ ♦♥ ts r s♥ ts ♥trt ♦♠♣tt♦♦ s ①♣t s ♣r♦♣rts ♦s t ♥s t♦ s t ♠ t♦ ♣r♦r♠t ♠♦ st♦♥

♥② t ♣r♦♣♦s ♠♦ ♥ s② ①t♥ t♦ t s ♦ ♦r♥ t♦r ts s♦♠ t♦♥ ♦♥str♥ts ♦♥ t ♣♥♥② strtr ♦ strt♦♥ ♦ ♠①♠♠ ♣♥♥② ♥ t♦ ♦t tt ts ♦♥str♥ts s♦♠t t ♦♠♥t♦r rsr ♦ t ♣♥♥② strtrs

Page 106: Model-based clustering for categorical and mixed data sets

♣tr

♦s str♥ t

♦♥t♦♥ ♣♥♥② ♠♦s

s ♣tr ♣rs♥ts ♦r s♦♥ ♦♥trt♦♥ t♦ t♠♦s r♠♦r ♣r♠tt♥ t♦ str t♦r t s ♦♥trt♦♥ ♦♥ssts ♥ ♠①tr ♠♦ r♦♣s t rs ♥t♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♦ ♦♦s ♣rs♠♦♥♦s ♠t♥♦♠ strt♦♥ r t r ♣r♠trs ♦rrs♣♦♥ t♦ ts ♠♦s ♥r♥ s s② ♣r♦r♠ ♥ ♠ ♦rt♠ t ♥ ♦ t ♠♦ st♦♥ s tt② ♥ ♥t ♣♣r♦①♠t♦♥ ♦ t ♥trt ♦♠♣tt ♦♦♠r ①♣r♠♥ts ♦♥ s♠t ♥ r t sts♥r♥ t ♠♥ rtrsts ♦ ts ♥ ♠①tr♠♦

r♠ ♣♦ rrst ts ♠♥ ts ♥ ♠ts

③③s rs t♥ r♦

♦ r♠ P♦

♥tr♦t♦♥

♥ ts ♣tr ♣rs♥t s♣rs ♠①tr ♠♦ r①s t ♦♥t♦♥♥♣♥♥ ss♠♣t♦♥ ♥ ♦rr t♦ ♦r♦♠ t ss s ② t t♥t ss♠♦ ♥ ♦r♦♠s t ♠♥ r ♦ t ♠ ♠♦ ♥ t♠♦ st♦♥ ♥ s② ♥ ♥t② ♣r♦r♠ ② ♦♥ t ♦♠♥t♦r♣r♦♠s s st♣ ♦s ♥♦t rqr t ♠ s♥ t ♥trt ♦♠♣tt♦♦ ♥ ♣rs② ♣♣r♦ rst② t ♠♦ st♦♥ ♥ ♣r♦r♠② ♠♠ ♦rt♠ r t ♠ s ♥♦t rqr ♦♥② t ♣r♠trs r♦♥② st♠t ♦r t st ♠♦ s♥♥t② ♠ts t ♦♠♣tt♦♥ t♠

Page 107: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦♥t♦♥ ♣♥♥② ♠♦s

s ♥ ♠♦ ♥♠ ♦♥t♦♥ ♦s ♦ rrr ♥ ts rt ②♠♠ r♦♣s t rs ♥t♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♦r ♦♥sr♥ t♠♥ ♦♥t♦♥ ♣♥♥s ♦r♦r t s♣ strt♦♥ ♦ t ♦ s ♠t♥♦♠ strt♦♥ ♣r ♠♦s s strt♦♥ ss♠s tt ♠♦t②r♦ss♥s ♥♠ ♠♦s r rtrst ♥ tt t ♦tr ♦♥s ♦♦ ♥♦r♠strt♦♥ s t ss♦t ♠t♥♦♠ strt♦♥ s ♣rs♠♦♥♦s s♥ts r ♣r♠trs r ♠t t♦ t ♣r♠trs ♦ t ♠♦s

s s♠♣ ♠①tr ♠♦ ♠♠ s ♦♦ ♥r ♥ t ♦♥ ♥ t♠♠ ♠♦ ♥s t ♠①tr ♠♦ t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ ♠ s♥ t ♦s ♠♥② ss tr♦ ♠♦③♥ ♦ t ♠♥ ♦♥t♦♥♣♥♥s ♥ t ♦tr ♥ t ♥s t ♠①tr ♠♦s r①♥ tsss♠♣t♦♥ s♥ ts ① strt♦♥ ♦ t ♦ rqrs ♣r♠trs ♦ttt s t ♠ ♠♦ t ♠♠ ♠♦ ♥ ♥tr♣rt s ♣rs♠♦♥♦srs♦♥ ♦ t ♦♥r ♠①tr ♠♦ ♥ t r♣rtt♦♥ ♦ t rs♥t♦ ♦s ♥s t ♦♥sr ♥trt♦♥s t strt♦♥ ♣r ♠♦s ♥t♦♦s ♥s s♣ strt♦♥ ♦r ♥trt♦♥ rtr♠♦r rst♥sss r ♠♥♥ s♥ t ♥trss ♣♥♥s r r♦t ♦t ② t♦♦♠♣♠♥tr② s t ♦ r ♥trt♦♥ ♥ t ss♦t ♠♦♥trt♦♥ tr♦ ♦t♦♥s ♥ ♣r♦ts ♦t tt t ♠♠ ♠♦ s ♦♠♣r♥s ♣♣r♦ s♥ t ♥s t ♠ ♠♦ ♥ ♣rt ♦ ts ♣rs♠♦♥♦s rs♦♥s ♣rs♥t ♥ t♦♥

♦r ① ♠♦ ♥♠r ♦ sss r♣rtt♦♥ ♦ t rs ♥t♦ ♦s ♥♥♠rs ♦ ♠♦s t ♠①♠♠ ♦♦ st♠t s ♦t♥ ♥ ♠ ♦rt♠ ♠♦ s st tr♦♣♦st♥s ♦rt♠ ♥ ts♦rt♠ s s s♠♣r ♥rts ♥ r♣rtt♦♥ ♦ t rs ♥t♦♦s ♥ ♥ ♥♠r ♦ ♠♦s ② ♦♥ tr♦♣♦sst♥s st♣ t s ♣r♦r♠♦r ① ♥♠r ♦ sss ♥ ♦s ♦♠♥t♦r ♣r♦♠s ♥♦ ② t st♦♥ ♦ t ♦s ♦ rs ♥ ② t st♠t♦♥ ♦ t ♥♠rs ♦ ♠♦ss ♦rt♠ s s ♦♥ t t tt t ♥trt ♦♠♣tt ♦♦ rqr ♦r t ♣t♥ ♣r♦t② ♦♠♣tt♦♥ ♦ t tr♦♣♦sst♥s ♥st s s♠♣r s ss ♥ ♥♦♥ ♠♦s tr♦ ② ♥♦r♠t ♦♥t ♣r♦r ♥② ts ♣♣r♦ s t♦ ♠♥ ♥ts t ♣r♠ts t♦ rt s ♦ t ♣♣r♦ t s ♠♥t♦♥ tt t ♦rst♠t♦♥ ♦ t♥♠r ♦ ♠♦s ② ts ♣♣r♦ s strt r♥ ♦r ♥♠r ①♣r♠♥tsrtr♠♦r t ♦s s t♦ ♣r♦r♠ ♥ ♥t ♠♦ st♦♥ ♥ rs♦♥♦♠♣tt♦♥ t♠ s♥ t ♣r♠trs r ♦♥② st♠t ♦r t ♥q st♠♦ s ts ♣♣r♦ s ♣♦ss ♥sr t♦ t ♦♠♥t♦r ♠♦ st♦♥♣r♦♠ s ♥♦♥ t♦ r ♥ ♦r ♦♥r ♠①tr ♠♦

trtr ♦ ts ♣tr s ♣♣r s ♦r♥③ s ♦♦s t♦♥ ♣rs♥ts t ♦♥t♦♥ ♦s ♦ t♦♥ s ♦t t♦ ♠①♠♠ ♦♦ st♠t♦♥ ♥ ♠ ♦rt♠ t♦♥ ♣rs♥ts t tr♦♣♦st♥s s♠♣r ♣r♦r♠♥ t ♠♦ st♦♥ tr♦ t ♥trt ♦♠♣tt♦♦ ♥ t♦♥ s♦ tt t ♣r♦♣♦s ♣♣r♦ ♦r ♦♠♣t♥ t ♥trt ♦♠♣tt ♦♦ rs t ss ♦ t ♣♣r♦ ♦r

Page 108: Model-based clustering for categorical and mixed data sets

①tr ♠♦ ♦ ♠t♥♦♠ strt♦♥s ♣r ♠♦s

♦r ♥♠r② ♠♣s③ t ♦♦ ♦r ♦ t tr♦♣♦st♥ss♠♣r ♥ t ①t② ♦ t ♠♠ ♠♦ ♦♥ s♠t t t♦♥ ♣rs♥tst♦ str ♥②ss ♦ ♦♦ t sts ♣r♦r♠ ② t ♣ ♦♦s ♦♥s♦♥ s r♥ ♥ tr ①t♥s♦♥s r sss ♥ t♦♥ ts rsts r ♣rt ♦ t rt ♥t ♠①tr ♠♦ ♦ ♦♥t♦♥ ♣♥♥s ♠♦st♦ str t♦r t ❬❱❪

①tr ♠♦ ♦ ♠t♥♦♠ strt♦♥s ♣r

♠♦s

♥ ♣r♦♣♦s ♠♦ rrr s ♦♥t♦♥ ♦s ♦ ♠♠ss♠s tt t rs ♥♣♥♥t② r♦♠ ♠①tr ♦ g ♦♠♣♦♥♥ts ♦ ♦♥t♦♥② ♥♣♥♥t ♦s r t r♣rtt♦♥ ♦ t rs ♥t♦ ♦s s qt♥ sss ♦ ♦♦s ♠t♥♦♠ strt♦♥ ♣r ♠♦s s ♠t♥♦♠ strt♦♥ ♥ r ♣r♠trs ♦rrs♣♦♥♥ t♦ t ♠♦s♦ t strt♦♥ ♦r ♣rs② t ♠♦s r ♥ s t ♦t♦♥s ♦ trst ♣r♦ts t ♦tr ♣r♠trs r q

♦♥t♦♥② ♥♣♥♥t ♦s q t♥ sss

r♣rtt♦♥ ♦ t rs s ss♠ t♦ q t♥ sss s♦ s t ♥♦tt♦♥s ♦ t ♠①tr ♠♦ ♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♥♥ t♦♥ ② ♦♠tt♥ t ♥①t♦♥ ♦♥ k

♣rtt♦♥ ♦ t rs qs t♥ sss r♣rtt♦♥ ♦ td t♦r rs xi = (①1i , . . . , ①

di ) ♥t♦ ♦s tr♠♥s ♣rtt♦♥ σ =

(σ1, . . . ,σ) ♦ 1, . . . , d ♥ s♦♥t ♥♦♥♠♣t② ssts s ♣rtt♦♥ ♥s♥ ♥rt t♦r rs xb

i = xσbi = (x

bhi ;h = 1, . . . ,mb) ♦t♥

② t ♦♥t♥t♦♥ ♦ t sst ♦ xi ss♦t t♦ σb r mb =∏

j∈σbmj

s t ♥♠r ♦ t ♠♦t② r♦ss♥s ♥t♦ ♦ b r xbi ss

s♥t ♦♥ s♥ xbhi = 1 ♥ i ts ♠♦t② h ♦r t ♥t♦r r t ♠♦t② r♦ss♥ h ♦ t ♥t rs t t♦t ♦ b ♥ xbhi = 0 ♦trs

r♣t ♥ ♠♦ s♣ ♠♦ s ♥ ② t ♥♠r ♦ ♦♠♣♦♥♥ts t r♣rtt♦♥ ♦ t rs ♥t♦ ♦s ♥ t ♥♠r ♦ ♠♦s ♦r ♠t♥♦♠ strt♦♥ ♦ t s ♥ ② t tr♣t ω = (g,σ, ℓ) rℓ = (ℓ1, . . . , ℓg) r♦♣s t ♥♠rs ♦ ♠♦s t ℓk = (ℓk1, . . . , ℓk) ♥ rℓkb s t ♥♠r ♦ ♠♦s ♦ xb

i ♦r ss k (t 0 < ℓkb < mb)

♥t♦♥ ①tr ♦ ♦♥t♦♥② ♥♣♥♥t ♦s q t♥ sss t♦r r xi s r♥ ② ♠♠ ♠♦ ♥ ② ω ♥ ♣r♠tr③

♦♥♦ t tt♣sr♦rr♣r♦t♦rr♦♣❴

Page 109: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦♥t♦♥ ♣♥♥② ♠♦s

② θ ts ♣ s rtt♥ s

p(xi;θ,ω) =

g∑

k=1

πkp(xi;αk,σ, ℓk) t p(xi;αk,σ, ℓk) =∏

b=1

p(xbi ;αkb, ℓkb),

r θ = (π,α) ♥♦ts t ♦ ♠①tr ♣r♠trs r π = (π1, . . . , πg)s t t♦r ♦ ss ♣r♦♣♦rt♦♥s t 0 < πk ≤ 1 ♥

∑gk=1 πk = 1 ♥ r

α = (α1, . . . ,αg) s t t♦r r♦♣s t ♣r♠trs ♦ t ♠t♥♦♠strt♦♥s ♣r ♠♦s t αk = (αk1, . . . ,αk)

t♥♦♠ strt♦♥ ♣r ♠♦s

❲ ♥♦ s♣② t ♠t♥♦♠ strt♦♥ ♣r ♠♦s ♦ t s ♥tr♦ ts♣r♠tr s♣ ♦r t♦ ♥ ts ♣

♥t♦♥ Pr♠tr s♣ ♦ ♠t♥♦♠ strt♦♥ ♣r ♠♦s tαkb = (αhkb;h = 1, . . . ,mb) t t♦r ♦ s③ mb ♥ t τkb t ♠♣♣♥r♦♠ 1, . . . ,mb t♦ 1, . . . ,mb ♦rr♥ t ♠♥ts ♦ αkb ② rs♥ s αkb ♥♦ts t ♣r♠trs ♦ t ♠t♥♦♠ strt♦♥ ♣r ℓkb ♠♦st♥ t s ♥ ♥ t ♦♥str♥ s♠♣① S(ℓkb,mb) ♥ s ♦♦s

S(ℓkb,mb) =

αkb : 0 ≤ αhkb ≤ 1,mb∑

h=1

αhkb = 1, α(ℓkj+1)

kb = . . . = α(mb)kb

.

r s t s♦rtr ♥♦tt♦♥ α(h)kb = α

τkj(h)

kb s♦ α(h)kb ≥ α

(h+1)kb (1 ≤ h < mb)

♥t♦♥ t♥♦♠ strt♦♥ ♣r ♠♦s ♥rt t♦rt x

bi s mb ♠♦ts ♥ ♦♦s ♠trt strt♦♥ ♣r ℓkb ♠♦s

ts ♣ s s♦ rtt♥ s

p(xbi ;αkb, ℓkb) =

mb∏

h=1

(

αhkb)x

bhi ,

r αkb = (αhkb;h = 1, . . . ,mb) ∈ S(ℓkb,mb) ♥ αhkb s t ♣r♦t② tt

♥ i ts ♠♦t② h ♦ t ♦♥t♥t t♦r r xbi

①tr ♠♦ ♦ ♦♥t♦♥ ♠♦s

♥t♦♥ ①tr ♠♦ ♦ ♦♥t♦♥ ♠♦s t♦r r xis r♥ ② ♠♠ ♠♦ ♥ ② ω ♥ ♣r♠tr③ ② θ ts ♣ s ♥ ②

p(xi;θ,ω) =

g∑

k=1

πk

b=1

mb∏

h=1

(

αhkb)x

bhi .

♠r ♠ ♠♦ s ♥ ♥ t ♠ ♠♦ s♥ t ♦♥t♦♥♥♣♥♥ ss♠♣t♦♥ t♥ t ♥t rs s ♥ ② ♣tt♥ ♦♥r ♣r ♦ s♦ d = ♥ σ = (1, . . . , ) ♥ ② ①♥ t ♥♠r ♦♠♦s s t ♥♠r ♦ ♠♦ts ♦ t rs ♠♥s ♦♥ ℓkj = mj − 1

Page 110: Model-based clustering for categorical and mixed data sets

①tr ♠♦ ♦ ♠t♥♦♠ strt♦♥s ♣r ♠♦s

Pr♦♣rts ♦ t ♠①tr ♠♦ ♣r ♦♥t♦♥ ♠♦s

♦ s ♦ ♥tr♣rtt♦♥ ♠♠ ♠♦ s t♦ s ♦ ♥tr♣rtt♦♥rst② t ♥trss ♣♥♥s ♦ rs q t♥ sss r ♠♣s③ ② t r♣rtt♦♥ ♦ t rs ♥t♦ ♦s ♥ ② σ ♦♥② t♥trss ♥ ♥tr♦ ♣♥♥s ♦ ♠♦ts ♣♦ss② r♥t t♥sss r s♠♠r③ ② t ♠♦s ♦t♦♥s ♥ ♣r♦ts

♦ ♦♠♣t tr♠s s♦rtr s♠♠r② ♦r strt♦♥ s s♦ ② s♥ t ♦♦♥ ♦♠♣t tr♠s κkb ♥ ρkb ♥ ♦♥ [0, 1] ②

κkb =ℓkb

mb − 1♥ ρkb =

ℓkb∑

h=1

α(h)kb .

② rt rs♣t② t ♦♠♣①t② ♥ t str♥t ♦ t ♥trss ♥ ♥tr♦ ♣♥♥s ♦r ♥st♥ t s♠r s κkb ♥ t rr s ρkb t ♠♦r♠ss ♥ rtrst ♠♦t② r♦ss♥s s t strt♦♥ ♥ t♠♦s r ♥tr♣rt s ♥ ♦r♦♥trt♦♥ t t ♥♦r♠ strt♦♥ ♠♦♥ t ♠♦t② r♦ss♥s

♥t② ♦t tt t r♣rtt♦♥ ♦ t rs r♥ts t ♠♦♥r ♥tt② s♥ t s q t♥ sss ♥ t ts ♦♥str♥tt rsts ♦ ❬❪ ♥ ♣♣ t♦ ♣r♦ t ♥r ♥tt② ♦ t♠♠ ♠♦ ts r ♥ ♥ ♣♣♥① s♣t t ♦♥str♥t t♦ ♦t s♠ r♣rtt♦♥ ♦ t rs ♥t♦ ♦s ♦r t sss t ♠♦ st②s① s ♦ t s♣ ♦ strt♦♥

♠r ♦ ♣r♠trs ♠♥ ♦ t ♦r♠r ♣rs♠♦♥♦s rs♦♥s ♦ t♠♠♦ ♦♥t♦♥ ♥♣♥♥ ♦ ♣r♦♣♦s ♥ ❬❪ s t♦ ♦♥sr ♦♥②♦♥ ♠♦ ♦r ♠t♥♦♠ strt♦♥ ♦ t ♥t r s t♦♥ r♥t ♦♥str♥ts ♦ qt② r t♥ t♥ t rs ♥♦r sss♥ t ♠♥② ♦ ts ♠♦s r ♥ ♥t♦ t ♠♦ ♠② ♦ ♠♠ ② ♣tt♥ = d ♥ ℓkb = 1 ♥ t♦♥ t ♠♠ ♠♦s ♥ ν♠♠ ♣r♠trs ♥ ②

ν♠♠ = (g − 1) +

g∑

k=1

b=1

ℓkb.

s ♠♦ ♦ t ♠♠ ♠② ♥ rqr ss ♣r♠trs t♥ ♠ ♠♦t ν♠ = (g−1)+g×∑

b=1(mb−1) ♣r♠trst♦ t ts ♥t♦ ♦♥t

t ♦♥t♦♥ ♣♥♥s

♣r♠tr③t♦♥ ♦ t ♦ strt♦♥

♥ ♣rs♠♦♥♦s rs♦♥s ♦ t ♠ ♠♦ ♥tr♦ ♥ ❬❪r ♠♥♥ s♥ ♠t♥♦♠ strt♦♥ s ①♣rss t t♦ t②♣s ♦♣r♠trs srt ♦♥ tr♠♥s t ♦t♦♥ ♦ t ♠♦ ♦ t strt♦♥♥ ♦♥t♥♦s ♦♥ s ts ♣r♦t② s t♦♥ ② s♥ t s♠

Page 111: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦♥t♦♥ ♣♥♥② ♠♦s

♣r♦♣♦s ♥ ♣r♠tr③t♦♥ ♦ t ♦ strt♦♥ ♥♦t ② (δkb,akb)s ♣r♠tr③t♦♥ tts t ♥tr♣rtt♦♥ ♥ t rt♥ ♦ t ♣r♦r ♥♣♦str♦r strt♦♥s rt t♦ t ♦ ♣r♠trs s t♦♥

♣r♠tr③t♦♥ srt ♣r♠tr δkb = δhkb;h = 1, . . . , ℓkb tr♠♥s t ♠♦ ♦t♦♥s s♥ δhkb ♥ts t ♠♦t② r♦ss♥ r t ♠♦h s ♦t t δhkb ∈ 1, . . . ,mb ♥ δhkb 6= δh

kb h 6= h′ ♦♥t♥♦s♣r♠tr akb = (ahkb;h = 1, . . . , ℓkb + 1) tr♠♥s t ♣r♦t② ♠ss ♦ t ℓkb♠♦s ② ts rst ℓkb ♠♥ts ahkb t h = 1, . . . , ℓkb) ♥ t ♣r♦t② ♠ss♦ t ♥♦♥♠♦ ② ts st ♠♥t aℓkb+1

kb ♣r♠tr akb s ♥ ♦♥ t♦♦♥ tr♥t s♠♣①

St(mb) =

akb : 0 ≤ ahkb ≤ 1, ∀h ≤ ℓkb + 1 ♥ ahkb ≥aℓkb+1kb

mb − ℓkb, ∀h ≤ ℓkb

.

♣r♠tr αkb ♥ t ♦♣ (δkb,akb) r rt ②

αhkb =

ah′

kb ∃h′ s tt δh′

kb = haℓkb+1

kb

mb−ℓkb♦trs

①♠♠ ♦♦ st♠t♦♥ ♥

♦rt♠

♠ t ① = (x1, . . . ,xn) t s♠♣ ♦♠♣♦s t n ♥♣♥♥t ♥ ♥t② strt ♥s ss♠♥ t♦ r♥ ② t ♠♠ ♠♦ r♦♠ tss♠♣ t ♠ s t♦ st♠t t ♠ ♦r ① ♠♦ ♥ ② ω

❲♥ ω s ♥♦♥ t ♠♠ ♠♦ ♥ ♥tr♣rt s ♠ ♠♦ ♣♣ ♦♥t ♦♥t♥t rs x

bi r ♦♥str♥ts r t♥ ♣r♠trs

s t ♠ ♥ s② ♦t♥ ② t ♦♦♥ ♠ ♦rt♠

Page 112: Model-based clustering for categorical and mixed data sets

♦ st♦♥ tr♦♣♦st♥s s♠♣r

trt♥ r♦♠ ♥ ♥t θ[0] trt♦♥ [r] s rtt♥ s st♣ ♦♥t♦♥ ♣r♦ts ♦♠♣tt♦♥

tik(θ[r]) =

π[r]k p(xi;α

[r]k ,σ, ℓk)

∑gk′=1 π

[r]k′ p(xi;α

[r]k′ ,σ, ℓk′)

.

st♣ ♠①♠③t♦♥ ♦ t ①♣tt♦♥ ♦ t ♦♠♣tt ♦♦♦

π[r+1]k =

n[r]k

n♥ α(h)[r+1]

kb =

n(h)[r]kb

n[r]k

(1 ≤ h ≤ ℓkb)

1−∑ℓkj

h′=1α(h′)[r+1]kb

mb−ℓkb♦trs

② s♥ t ♥♦tt♦♥s n[r]

k =∑n

i=1 tik(θ[r]) ♥ nh[r+1]

kb =∑n

i=1 tik(θ[r])x

bhi

♦rt♠ ♠ ♦rt♠ t♦ ♦t♥ t ♠ ♦ ♠♠ ♠♦

♠r ♥ t ♥t♦♥ τkb ♦t tt t t ♠ st♣ ♦ trt♦♥ [r] t♥t♦♥ τkb s r♥ s t rs♥ ♦rr♥ ♥t♦♥ ♦ t nh[r+1]

kb ♥ ♦ss t♦ ♥ n(h)[r+1]

kb t n(h)[r+1]kb ≥ n

(h+1)[r+1]kb

♦ st♦♥ tr♦♣♦st♥s

s♠♣r

Pr♦r strt♦♥s ❲ ss♠ tt p(g) = 1gmax

♦r g = 1, . . . , gmax ♥ ttp(σ) r♠♥ tt g ♥ σ r ♥♣♥♥t ♥ p(ℓ|g,σ) ♦♦ ♥♦r♠ strt♦♥s

♠ ♠ s t♦ ♦t♥ t ♠♦ ω = (g, σ, ℓ) s t rst ♣♦str♦r♣r♦t②

ω = argmaxg,σ,ℓ

p(①|g,σ, ℓ) = argmaxg,σ,ℓ

p(g,σ, ℓ|①).

t gmax ♠♦s ♥♦t ② ω(g) = (g,σ(g), ℓ(g)) ♦r g = 1, . . . , gmax r

(σ(g), ℓ(g)) = argmaxσ,ℓ

p(①|g,σ, ℓ) = argmaxσ,ℓ

p(σ, ℓ|①, g).

st ♠♦ s s♦ ♥ s

ω = argmaxg

p(ω(g)|①),

♥ s ♦♥ ② ♣♣②♥ t ♣♣r♦①♠t♦♥ ♠♦♥ t♦s gmax st ♠♦s

Page 113: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦♥t♦♥ ♣♥♥② ♠♦s

♥ ♥ ①st sr strt② s ♥♦t ♦ ♦r t♦ ♦rrt rs♦♥srst② t ♥♠r ♦ ♦♣s (σ, ℓ) ♥ ①ss② ♥ s♦♥② tst♠t♦♥ ♦ t ♠ ♦r ♦ t♠ s ♥ ♥♥ssr② st ♦ ♦♠♣tt♦♥t♠ tr♦♣♦st♥s s♠♣r strt② ♦r♦♠s ts t♦ rst t s♠ t♠ s ♥♦ sr

♦r ① ♦ g t ♦♣ (σ(g), ℓ(g)) s st♠t ② t ♦♦♥ tr♦♣♦st♥s s♠♣r ❬❪ ♥ p(σ, ℓ|g,①) s stt♦♥r② strt♦♥

s ♦rt♠ s p(σ, ℓ|g,①) s ♠r♥ stt♦♥r② strt♦♥ trt♥r♦♠ ♥ ♥t (σ[0], ℓ[0]) trt♦♥ [s] s rtt♥ s

θ[s+1] ∼ θ|ω[s],①, ③[s]

③[s+1] ∼ ③|ω[s],①,θ[s+1]

(σ[s+1], ℓ[s+1]) ∼ σ, ℓ|ω[s],①, ③[s+1],

r ω[s] = (g,σ[s], ℓ[s])

♦rt♠ tr♦♣♦st♥s s♠♣r t♦ ♦t♥ ω(g)

♠r ♥ t ♠♦ s♠♣♥ rt s♠♣♥ r♦♠ s ts st♣ s s♦ ♣r♦r♠ ② ♦♥ trt♦♥ ♦ tr♦♣♦sst♥s ♦rt♠♦s t stt♦♥r② strt♦♥ s p(σ, ℓ|g,①, ③[r+1]) ♦r ts r ♥ ♥t♦♥

♠♣♥ ♦ t ss ♠♠rs♣s s t ♦sr t r ♥♣♥♥tt ♦♥t♦♥ strt♦♥ ♦ ③ s ss ♥ s rtt♥ s

p(③|ω,①,θ) =n∏

i=1

p(zi|ω,xi,θ) t p(zi|ω,xi,θ) =g∏

k=1

(tik(θ))zik .

♥ ts st♦♥ rst② t t ♦♥t♦♥ strt♦♥s s♠♣♥ t ♣r♠trs ♥♦t ② ♥str♠♥t ♠♥ts ② s♥ t ♦ ♣r♠tr③t♦♥♥ ♥ t♦♥ ♥ s♦♥② t t s♠♣♥ ♦ (σ, ℓ) ♦♥sr st ♥trst ♠♥ts

♠♣♥ ♦ t ♥str♠♥t ♠♥ts

❲ ♥♦ t t s♠♣♥ r♦♠ ♥ ② p(θ|ω[s],①, ③[s])

Pr♦r ss♠♣t♦♥ ❲ ss♠ t ♣r♦r ♥♣♥♥ t♥ t ss ♣r♦♣♦rt♦♥s ♥ t ♣r♠trs ♦ t ♦ strt♦♥s ♦ t ♣r♦r ♦ t ♦♣r♠tr s rtt♥ s ♦♦s

p(θ|ω) = p(π|ω)

g∏

k=1

mb∏

b=1

p(αkb|ω).

Page 114: Model-based clustering for categorical and mixed data sets

♦ st♦♥ tr♦♣♦st♥s s♠♣r

♦t tt ts ♣r♦♣rt② ♦ ♦♥t♦♥ ♥♣♥♥ s ♣t ② t strt♦♥ ♦ θ♦♥t♦♥② ♦♥ (ω,①, ③) s♥

p(θ|ω,①, ③) = p(π|ω,①, ③)g∏

k=1

mb∏

b=1

p(αkb|ω,①, ③).

Pr♦r ♥ ♣♦str♦r strt♦♥s ♦ π r②s ♥♦♥ ♥♦r♠t ♣r♦rstrt♦♥ ♦r ♠t♥♦♠ s ♦♥t rt strt♦♥ ❬♦❪ ♦t ♣r♦r ♥ t ♣♦str♦r strt♦♥s ♦ π ❬❪ r rs♣t② ♥ ②

π|ω ∼ Dg

(1

2, . . . ,

1

2

)

♥ π|ω,①, ③ ∼ Dg

(1

2+ ♥1, . . . ,

1

2+ ♥g

)

,

r ♥k =∑n

i=1 zik

Pr♦r strt♦♥ ♦ αkb ❲ ♥♦ s t ♣r♠tr③t♦♥ ♦ t ♦ strt♦♥ (δkb,akb) ♥ ♥ t♦♥ ❲ ss♠ t ♥♣♥♥ t♥t ♣r♦r ♦ δkb ♥ ♦ akb s♦

p(αkb|ω) = p(δkb|ω)p(akb|ω).

❲ s ♥♦r♠ strt♦♥ ♠♦♥ t ♠♦ ♦t♦♥s ♥ ♦♥t tr♥t rt strt♦♥ s ♣r♦r ♦ akb s♦

p(δkb|ω) =

(

mb

ℓkb

)−1

♥ akb|ω ∼ Dtℓkb+1

(

γ1kb, . . . , γℓkb+1kb ;mb

)

,

r t γhkb r t ♣r♠trs ♦ t tr♥t rt strt♦♥ s♦ ttakb|ω ∈ St(mb) ❲ ♥♦ ① γhkb = 1 stt♦♥ s ♥ ♥ ♣♣♥① ♣r♦♣♦s ♣r♦r s s♦ ② ♥♦r♠t s♥ t s ♥ ♥♦r♠ strt♦♥

P♦str♦r strt♦♥ ♦ αkb ♣♦str♦r strt♦♥ ♦ αkb s rtt♥ s

p(αkb|ω,①, ③) = p(δkb|ω,①, ③)p(akb|ω, δkb,①, ③).

strt♦♥ ♦ δkb|ω,①, ③ s ♠t♥♦♠ ♦♥ t t♦♦ ♠♥② s t♦ ♦♠♣t t δkb = δhkb;h = 1, . . . , ℓkb t st ♦♥t♥♥ t ♥s ♦ tℓkb rst s ♦ ♥hkb =

∑ni=1 zikx

bhi ♦rr

∀h ∈ 1, . . . , ℓkb − 1, ♥δhkbkb ≥ ♥

δh+1kbkb .

❲ ss♠ tt t r♥ t♥ t ♠♦ ♣r♦ts ♥ t ♥♦♥♠♦♣r♦ts r s♥♥t ♦ ♥ ♣♣r♦①♠t t ♦♥t♦♥ strt♦♥

p(akb|ω) ∝∏ℓkb+1

h=1(ahkb)

γh

kb−1

1

ah

kb≥

aℓkb+1

kb

mb−ℓkb

Page 115: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦♥t♦♥ ♣♥♥② ♠♦s

♦ δkb ② r ♥ δkb s ♣♣r♦①♠t♦♥ s str♥t♥ ② t st ♦♥r♥ s♣ ♦ t srt ♣r♠trs ❬❪ ♦♥r♥♥ ♥♦ akb s ts ♣r♦r s♦♥t ts ♦♥t♦♥ strt♦♥ s ①♣t② ♥ s

akb|ω, δkb,①, ③ ∼ Dtℓkb+1

(

1 + ♥(1)kb , . . . , 1 + ♥

(ℓkj)

kb , 1 + ♥ℓkbkb ;mb)

,

r ♥(h)kb s t ht rr ♦ t st ♥hkb;h = 1, . . . ,mb ♥ ♥ℓkbkb =

♥k −∑ℓkb

h=1 ♥(h)kb

♠♣♥ ♦ ♥ ♠♦ (σ[s+1], ℓ[s+1])

♥ s♠♣♥ ♦ ω[s+1] = (g,σ[s+1], ℓ[s+1]) ♦r♥ t♦ s ♣r♦r♠ ② ♦♥ trt♦♥ ♦ t ♦♦♥ ♠♠ ♦rt♠ ♦s t stt♦♥r② strt♦♥ s p(σ, ℓ|g,①, ③[r+1]) s ♦rt♠ s ♥ t♦ st♣s rst② ts♠♣s ② ♦♥ trt♦♥ ♦ tr♦♣♦sst♥s ♦rt♠ ♥ r♣rtt♦♥ ♦t rs ♥t♦ ♦s ♥ t ♠♦ ♥♠r ♦ t ♠♦ ♦s ♥♦t rs♣t② ② σ[s+1] ♥ ℓ[s+1/2] ♦♥② t s♠♣s t ♠♦ ♥♠r ♦ ♦② ♦♥ ♠♠ trt♦♥ s t s♠♣♥ ♦ ω[s+1] s ♦♠♣♦s ♥t♦ t t♦♦♦♥ st♣s

s ♦rt♠ s p(σ, ℓ|g,①, ③[s+1]) s stt♦♥r② strt♦♥ t ttrt♦♥ [s] ♦ ♦rt♠ t s♠♣♥ ♦ ω[s+1] s ♣r♦r♠ ♦r♥t♦ ♦t ♦♦♥ st♣s

(σ[s+1], ℓ[s+1/2]) ∼ σ, ℓ|ω[s],①, ③[s+1]

ℓ[s+1] ∼ ℓ|ω[s+1/2],①, ③[s+1],

r ω[s+1/2] = (g,σ[s+1], ℓ[s+1/2])

♦rt♠ ♦rt♠

tr♦♣♦sst♥s ♦rt♠ t♦ s♠♣ ω[s+1/2]

s♠♣♥ ♦ ω[s+1/2] s ♣r♦r♠ ② ♦♥ trt♦♥ ♦ t tr♦♣♦sst♥s♦rt♠ ♥t♦ t♦ st♣s rst② t ♥str♠♥t strt♦♥ q(.;ω[s])♥rts ♥t ω⋆ = (g,σ⋆, ℓ⋆) ♦♥② ω[s+1] s s♠♣ ♦r♥ t♦ t♣t♥ ♣r♦t②

♥str♠♥t strt♦♥ ♥str♠♥t strt♦♥ q(.;ω[s]) s♠♣s ω⋆

♥ t♦ st♣s rst st♣ ♥s t ♦ tt♦♥ ♦ ♦♥ r ♥ ♣rtσ⋆ s ♥♦r♠② s♠♣ ♥ V (σ[s]) = σ : ∃!b s b ∈ σ

[s]j ♥ b /∈ σj s♦♥

st♣ ♥♦r♠② s♠♣s t ♠♦ ♥♠rs ♠♦♥ ts ♣♦ss s ♦r t♠♦ ♦s ℓ⋆kj = ℓ

[s]kj ♦r ♥♦♥♠♦ ♦s j s tt σ[s]

j = σ⋆j

Page 116: Model-based clustering for categorical and mixed data sets

♦ st♦♥ tr♦♣♦st♥s s♠♣r

♣t♥ ♣r♦t② ♣t♥ ♣r♦t② λ[s] s ♥ ②

λ[s] = min

p(①, ③[s+1]|ω⋆)

p(①, ③[s+1]|ω[s])

q(ω[s];ω⋆)

q(ω⋆;ω[s]); 1

.

♦♠♣tt♦♥ ♦ λ[s] ♥♦s t♦ ♦♠♣t t ♥trt ♦♠♣tt ♦♦ ❲ ♥♦ sr ♦ t♦ s♦ ts ♣r♦♠ t♦t s♥ t s

♣♣r♦①♠t♦♥ ♦r s♥ t♦♦ ♠ t♠ ♦♥s♠♥ ♠♠ ♠t♦s s♠♣♥♦ ω[s+1/2] s s♦ ♣r♦r♠ ② t ♦♦♥ tr♦♣♦sst♥s ♦rt♠

s ♦rt♠ s p(σ, ℓ|g,①, ③[s+1]) s stt♦♥r② strt♦♥ trt♥r♦♠ ♥ ♥t θ[0] trt♦♥ [r] s rtt♥ s

ω⋆ ∼ q(ω;ω[s])

ω[s+1/2] =

ω⋆ t ♣r♦t② λ[s]

ω[s] t ♣r♦t② 1− λ[s].

♦rt♠ tr♦♣♦sst♥s ♦rt♠

♦rt♠ t♦ s♠♣ ℓ[s+1]

s st♣ ♦s s t♦ ♥rs ♦r rs t ♠♦ ♥♠r ♦ ♦ ②♦♥ t trt♦♥ ♦ ℓ[s+1]

kb s s♠♣ r♦♠ p(ℓkb|ω[s+1/2],①, ③[s+1]) ♥ ②

p(ℓkb|ω[s+1/2],①, ③[s+1]) ∝

p(①b|③[s+1], ℓkb) |ℓkb − ℓ[s+1/2]kb | < 2

♥ ℓkb /∈ 0,mb.0 ♦trs

r ①b = (xbi ; i = 1, . . . , n) s ts ♦rt♠ rqrs t ♦♠♣tt♦♥ ♦

p(①b|③, ℓkb) ♥ ②

p(①b|③, ℓkb) =∫

S(ℓkb,mb)

mb∏

h=1

(αhkb)♥hkbdαkb

♥ tt t ♥♦

♥trt ♦♠♣tt ♦♦

♥trt ♦♠♣tt ♦♦ s ♥ s

p(①, ③|ω) = p(③|ω)

g∏

k=1

b=1

p(①b|③, ℓkb).

♦t tt t q♥tts p(①, ③|ω) ♥ p(①j|③, ℓkb) r rs♣t② rqr t♦ ♦♠♣t t ♣t♥ ♣r♦t② ♦ t tr♦♣♦sst♥s ♦rt♠ ♥ ② ♥ t♦ s♠♣ t ♥♠r ♦ ♠♦s r♦♠ t ♥ t ②

Page 117: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦♥t♦♥ ♣♥♥② ♠♦s

♣♣r♦①♠t♦♥s ♦r ♥st♥ t ♥trt ♦♠♣tt ♦♦ s♣♣r♦①♠t ②

ln p(①, ③|ω) = ln p(①, ③|θ⋆,ω)− ν

2lnn+O(1),

r θ⋆ s t ♠①♠♠ ♦♠♣tt ♦♦ st♠t ♦r ts ♥ ♦♣♣r♦①♠t♦♥ s ♦♥② s②♠♣t♦t② tr ♥ ♥ ♦rst♠t t ♠♦ ♥♠rss t♦♥ s ③|ω ♦♦s ♥♦r♠ strt♦♥ ♠♦♥ t ♣♦ss♣rtt♦♥s ♣r♦♣♦s t♦ ♦♠♣t p(①b|③, ℓkb) t♦ ♦t♥ p(①, ③|ω) s ♦♠♣tt♦♥ s ♥♦t s② s♥ αkb s ♥ ♦♥ S(ℓkb;mb) ♥ ♥♦t ♦♥ t ♦ s♠♣①♦ s③ ℓkb ①♣t ♥ ℓkb = mb − 1 ♥ s s ♥ s t ♣♣r♦ ♦t ♠ ♠♦ ❬❪ ♥ ①♣t ♦r♠ s ♥ ♥ t ♦♦♥ ♣r♦♣♦st♦♥② ♣r♦r♠♥ ♥ ①t ♦♠♣tt♦♥ ♦ t ♥tr ♦r t ♦♥t♥♦s ♣r♠trs♥ ♥ ♣♣r♦①♠t♦♥ ♦♥ t srt ♦♥s ♦r t ♣r♦♦ s ♥ ♣♣♥①

Pr♦♣♦st♦♥ ♥trt ♦♠♣tt ♦♦ s ♣♣r♦①♠t ② ♥t♥ t s♠ ♦r t srt ♣r♠trs ♦ t ♠♦s ♦t♦♥s ♥ ② ♣r♦r♠♥ t ①t ♦♠♣tt♦♥ ♦♥ t ♦♥t♥♦s ♣r♠trs s♦

p(xb|z, ℓkb) ≈(

1

mb − ℓkb

)nℓkbkb

ℓkb∏

h=1

Bi(

1mb−h+1

; n(h)kb + 1; nhkb + 1

)

mb − h,

r Bi(x; a, b) = B(1; a, b)−B(x; a, b) ♥ r B(x; a, b) s t ♥♦♠♣t t♥t♦♥ ♥ ② B(x; a, b) =

∫ x

0wa(1− w)bdw

r♦♠ t ♣r♦s ①♣rss♦♥ t s strt♦rr t♦ ♦t♥ p(①, ③|ω)

♠r ①♣r♠♥ts ♦♥ s♠t t sts

♥trt ♦♠♣tt ♦♦ ♦♠♣rs♦♥ ♦ ♦t

♣♣r♦s

♠ r♥ ts ①♣r♠♥t t t ss ♦ t rtr♦♥ ♦r tst♦♥ ♦ t ♥♠r ♦ ♠♦s ♥ t ♥ ♥ ② t ♣r♦♣♦s ♦♠♣tt♦♥♦ t ♥trt ♦♠♣tt ♦♦

t ♥rt♦♥ ❲ ♥t t♦ ♦♠♣r ♦t ♣♣r♦s ♦r t st♦♥ ♦ t♥♠r ♦ ♠♦s ♦ s♠t s♠♣s ♦♠♣♦s t n ♥s rs♥r♦♠ ♠t♥♦♠ strt♦♥ ♣r ♠♦s Ms(r, r, r,

1−3rs−3

, . . . , 1−3rs−3

) t s ♠♦ts ♥ tr ♠♦s ♥ ♣r♦t② r ♦r r♥t s③s ♦ s♠♣ 105

s♠♣s r ♥rt t r♥t s ♦ (r, s)

sts r s ♦♠♣rs♦♥ t♥ t ♣r♦♣♦s ♣♣r♦ ♥ t ♣♣r♦①♠t♦♥ ♦r t st♦♥ ♦ t ♥♠r ♦ ♠♦s ♣r♦♣♦srtr♦♥ ♦t♥s ttr rsts t♥ t rtr♦♥ ♥ t ♦r st stt♦♥s♦r t r s♠♣ s③s rtr♠♦r t ♦s t♦ ♥r ♦rst♠ts t ♥♠r

Page 118: Model-based clustering for categorical and mixed data sets

♠r ①♣r♠♥ts ♦♥ s♠t t sts

0 100 200 300 400 500

0.0

0.2

0.4

0.6

0.8

1.0

sample size

prob

abili

ty

0 100 200 300 400 500

0.0

0.2

0.4

0.6

0.8

1.0

sample size

prob

abili

ty

0 100 200 300 400 500

0.0

0.2

0.4

0.6

0.8

1.0

sample size

prob

abili

ty

0 100 200 300 400 500

0.0

0.2

0.4

0.6

0.8

1.0

sample size

prob

abili

ty

r Pr♦t② tt t rtr♦♥ r♣rs♥t ♥ ♥ ♥s ♥t ♣r♦♣♦s ♣♣r♦ r♣rs♥t ♥ ♦ r ♥s st t tr ♥♠r ♦♠♦s r♣rs♥t ♥ ♣♥ ♥ ♥ ♦rst♠t t r♣rs♥t ♥ ♦tt ♥ r s r s r s r s

♦ ♠♦s ♥② ts rt② s s♠r t♥ t rtr♦♥ ♦♥ ❲ ♥tr ♥♦♥t♦ ♠♦r s♣ ♦♠♠♥ts

♥ s ♠♦s r ♣r♦t② ♠ss ♥ t② r s② tts♥ tr r ♠♦ts s ♦t rtr t s♠ ♦r s♥t② ♥ t tr ♥♠r ♦ ♠♦s t ♣r♦t② ♦s t♦ ♦♥ ♥ ♦r s♠s♠♣s

❲♥ t ♠♦ ♣r♦ts rs s t s ♠♦r t t♦ ♥t②t♠ ♥ s s t rtr♦♥ ttr ♥s t tr ♥♠r ♦ ♠♦st♥ t ♣r♦♣♦s ♣♣r♦ ♦r t s♠ s♠♣s s③ ♦r t♥ ♦rt rtr♦♥ s ♠♦rt rs t♦ ♦rst♠t t ♥♠r ♦ ♠♦s

Page 119: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦♥t♦♥ ♣♥♥② ♠♦s

t ♣r♦♣♦s ♣♣r♦ ♥rst♠ts ts ♥♠r ♥ t s r♦♥ ❲♥ ts♠♣ s③ s rr t♥ t ♣r♦♣♦s ♣♣r♦ ♦t♥s ttr rsts ♥t ♥s tr ♥♠r ♦ ♠♦s ♠♦st ②s t rtr♦♥ ♣s ♥♦rst♠t♦♥ rs

t ♥♠r ♦ ♠♦ts ♥rss s t♥ t ♣r♦♠ ♦♠s rr♥ t ♣r♦♣♦s ♣♣r♦ s♦ s♦s ts ♥trst s♥ t rtr♦♥ s str♦♥②s ♥ s s rtr♦♥ ♣s ts s ♥ ♦r r t st t ♣r♦♣♦s ♣♣r♦ ♠♦st ②s ♥s t tr ♥♠r ♦ ♠♦s ♥ ts♠♣ s③ s rr t♥

♥② ♥♦t tt ♥ t ♠♦r ♦♠♣① stt♦♥s ♥ s ♣r♦t② ♠ss ♦r t ♠♦s ♥ r ♥♠r ♦ ♠♦ts t ♣r♦♣♦s ♣♣r♦♥rst♠ts t ♥♠r ♦ ♠♦s ♥ t s♠♣ s③ s s♠ t♥ ♦♥rst♦ t tr ♠♦ s ♥ t s♠♣ s③ ♥rss ♦t tt ♥ s st s ♦ t rtr♦♥ st②s s♥♥t ♥ ♦r r t st

s ♦♥ ts ①♣r♠♥t t ♣r♦♣♦s rtr♦♥ ♣♣rs s t ♠♦st r♥ts♥ ts s②♠♣t♦t ♦r s ttr t♥ t s②♠♣t♦t ♦r ♦ t

rtr♦♥ ♥ t ♥r ♦rst♠ts t ♥♠r ♦ ♠♦s ♥ ts rt② ss♠r t♥ t rt② ♦ t rtr♦♥

♠t♦♥ t s♣ ♠♦

♠ r♥ ts ①♣r♠♥t t t ♦♦ ♦r ♦ t ♦rt♠s♠ ♦rt♠ ♥ tr♦♣♦st♥s s♠♣r ♦r ♣r♦r♠♥ t st♠t♦♥♦ t ♠ ♥ t ♠♦ st♦♥ ♦ t r ♥rt ♦r♥ t♦ ♠♠

♠♦ t♥ t ♠♦ ♥ t ♠ r st♠t qt② ♦ t st♠t♦♥s tr♠♥ ② t r r♥ ❲ s♦ tt ts q♥tt② ♦♥rs t♦ ③r♦ ♥ t s♠♣ s③ ♥rss ♦ ♦♥ t♦ t ♦♦ ♦r♦ ♦t ♦rt♠s

t ♥rt♦♥ t st ♦ s① rs t tr ♠♦ts s ♥rt♦r♥ t♦ ♦♠♣♦♥♥t ♠♠ ♠♦ t t ♦♦♥ ♣r♠trs

σ = (1, 2, 3, 4, 5, 6), ℓkj = 2, π = (0.5, 0.5), αkj = (0.4, 0.4, 0.2/7).

♠♦s r ♦t t r♥t ♠♦t② r♦ss♥s ♦r ♦t sss

sts ♦r r♥t s ♦ n = (50, 100, 200, 400, 800) s♠♣s r ♥rt r r♥ s ♦♠♣t t♥ t tr ♥ tst♠t ♣r♠trs ♣rs♥ts t ♠♥ ♦ ts r♥

s t r r♥ ♦♥rs t♦ ③r♦ ♥ t s♠♣ s③ ♥rss ♠ tt t st♠t strt♦♥ ♦♥rs t♦ t tr ♦♥ s ♦♥ t♦ t ♦♦ ♦r ♦ t st♠t♦♥ ♦rt♠

♠t♦♥ t ♠ss♣ ♠♦

♠ r♥ ts ①♣r♠♥t ♥r♥ tt t ①t② ♦ t ♠♠ ♠♦♦s t t♦ ♣ ♦♦ rsts ♥ t ♠♦ s ♠ss♣ s s♠t

Page 120: Model-based clustering for categorical and mixed data sets

♠r ①♣r♠♥ts ♦♥ s♠t t sts

n ♠♥

s

♥ ♥ st♥r t♦♥ ♦ t r r♥ ♦♠♣t t♥ t tr ♣r♠trs ♦ t s♣ ♠♦ ♥ t ♠①♠♠ ♦♦ st♠ts ss♦t t♦ t ♠♦ st ② t tr♦♣♦st♥s♦rt♠ ♦r r♥t s♠♣ s③s

s♠♣s ♦r♥ t♦ ♦♠♣♦♥♥t ♠①tr ♠♦ r t ♥trss ♣♥♥s r r♥t ♦r ♦t ♦♠♣♦♥♥ts t♥♥ ♣r♠tr ♦s s t♦ ♠♦②t str♥t ♦ t ♥trss ♣♥♥s ♥ t ss ♦r♣♣♥ rsts♦ t ♠♠ ♠♦ r ♦♠♣r t♦ t♦s ♦ t ♠ ♠♦

t ♥rt♦♥ t st ♦ s③ s s♠♣ r♦♠ t ♦♦♥ ♦♠♣♦♥♥t♠①tr ♠♦ ♦ ♠♥s♦♥ s①

p(x;θ) = 0.53∏

h=1

p(x2h−1,x2h;θ) + 0.5 p(x1;θ)p(x6;θ)2∏

h=1

p(x2h,x2h+1;θ),

t p(xj,xj+1;θ) = p(xj;θ)(

λ1xj=xj+1 + (1− λ)p(xj+1;θ))

♥ t p(xj;θ) =∑3

h=1(1/3)xjh s ♥ λ = 0 t s♠♣ s ♥rt ② ♥♦r♠ strt♦♥

♥ sss r ♦♥s rr s t t♥♥ ♣r♠tr λ t rr r t♥trss ♣♥♥s ♥ t ss s♣rt♦♥ ♦t tt ♠♠ s ♥♦t t tr♠♦ s♥ t ♦♥t♦♥② ♦rrt rs r ♥♦t t s♠ ♥ ♦t sss

sts ♦r r♥t s ♦ λ = (0.2, 0.4, 0.6, 0.8) s♠♣s r ♥rt r r♥ ss♦t t♦ t ♠♦ t t st ♥♠r ♦sss st ② t rtr♦♥ ♠♦♥ g = 1, ..., 4 s ♦♠♣t ♣rs♥ts t rsts ♦t♥ ② t ♠♠ ♥ t ♠ ♠♦s

λ ♠♠ ♠

r r♥ ♥ ♠♥ ♦ t ♥♠r ♦ sss ♦t♥② ♠♠ ♥ ♠

rr s λ t rr s t r r♥ ♦r ♦t ♠♦s♦r t ①t② ♦ t ♠♠ ♠♦ ♦s t♦ ♣ ♥ ♣t ♦ tr r♥ ts r♥ r♦s r♠t② str t t♠ ♠♦ rtr♠♦r ♥ t sss r s♣rt r ♦ λ t♠♠ ♠♦ ♥s ♠♦r ♦t♥ t tr ♥♠r ♦ sss t♥ t ♠ ♠♦

Page 121: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦♥t♦♥ ♣♥♥② ♠♦s

♥②ss ♦ t♦ r t sts

♦r ♦t ♣♣t♦♥s t st♠t♦♥ ♦ t ♠♠ ♠♦ s ♣r♦r♠ ② t ♣ ♦♦s ♦t t sts r ♥ ♦♦s ♦♣ ② t t♦rs

rs str♥

t ❲ st② ♦♦ t st sr♥ ♣♥s srs ② ♣♠ ♥ ①tr♥ ♠♦r♣♦♦ rtrsts ♣rs♥t ♥ ❬r❪s srs r ♥t♦ tr ss♣s r♦s rs r♠♥r rs ♥ srs rs

rs mj ♠♦ts♦r ♥♦♥ ♦♥t♥♦s

②r♦s ♥♦♥ r② ♣r♦♥♦♥s t ♥ t ♥ t

♦rr ♥♦♥ ♠♥②♥r ♠ ♠

Prs♥tt♦♥ ♦ t ♣♠ ♥ ①tr♥ ♠♦r♣♦♦ rssr♥ t ♣♥s

①♣r♠♥t stt♥s ss♣s ♠♠rs♣s ♦ t ♥s r ♥♦r g = 1, . . . , 6 t ♠ ♦ t ♠ ♠♦ s ♦t♥ ② ♥t③t♦♥s ♦ ♥ ♠♦rt♠ ♥s ♦ trt♦♥s r ♣r♦r♠ ♦r t ♠♦ st♦♥♦ t ♠♠ ♠♦ ♦♦ ② ♥t③t♦♥s ♦ ♠ ♦rt♠ t♦ ♥ t ♠

sts ♣rs♥ts t s ♦ t rtr♦♥ ♦r ♦t ♠♦s ♥r♥t ♥♠rs ♦ sss ♥ ♦t ♠♦s st t♦ ♦♠♣♦♥♥ts t s♦ t rtr♦♥ r ttr ♦r t ♠♠ ♠♦ t♥ ♦r t ♠ ♠♦ ♦r t♥♠rs ♦ sss s t ♠♠ ♠♦ ttr ts t t t♥ t ♠ ♠♦

g ♠♠ ♠

❱s ♦ t rtr♦♥ ♦r r♥t ♥♠rs ♦ sss ♦t♥ ②t ♠♠ ♥ t ♠ ♠♦s ♦ ♥ts t st s ♦ ts rtr♦♥

♦r♥ t♦ s♣②♥ t ♦♥s♦♥ ♠tr① t♥ t st♠t♣rtt♦♥s ♥ t ss♣s ♠ tt t rs r ♠♦r r♥t t♥t t♦ ♦tr ss♣s ♥ ♦t ♠♦s t t rs ♥ ss t st♠t ♣rtt♦♥s ② ♦t ♠♦s r s♠r r♠r tt t ♠♠ ♠♦ts ss ♦tr ss♣s ♥ ts ss t♥ t ♠ ♠♦

Page 122: Model-based clustering for categorical and mixed data sets

♥②ss ♦ t♦ r t sts

♠♠ ♠

ss ss ss ss r♦s

r♠♥r rs

♦♥s♦♥ ts t♥ t ss♣s ♥ st♠t ♣rtt♦♥ ♥t♦t♦ sss

r s♣②s t srs sttr♣♦t ♦♥ t rst ♦rrs♣♦♥♥ ♥②ss ♣♥ ♥ ♥ts t ss♣s ❲ ♥♦t tt t rs r ♥ t s♠♦t♦♥ ♦tt♦♠ t ♦r t rst ♣r♥♣ ♦rrs♣♦♥♥ ♠♣ ❲ s♣② t♣rtt♦♥ ♦rrs♣♦♥♥ t♦ t st ♠♦ t ♠♠ ♠♦ t t♦ ♦♠♣♦♥♥ts♥ r ♦t tt ♦r ♦t ♠♦s t rst ♣r♥♣ ♦rrs♣♦♥♥ ①s♦s t♦ ♥ sst♦♥ r

−1.0 −0.5 0.0 0.5 1.0 1.5

−1.

0−

0.5

0.0

0.5

1.0

1.5

2.0

First principal correspondence analysis

Sec

ond

corr

espo

nden

ce a

naly

sis

Dichrous Lherminieri Subalaris

−1.0 −0.5 0.0 0.5 1.0 1.5

−1.

0−

0.5

0.0

0.5

1.0

1.5

2.0

First principal correspondence analysis

Sec

ond

corr

espo

nden

ce a

naly

sis

Class 1 cmm Class 2 cmm

r rs ♦♥ t rst ♣r♥♣ ♦rrs♣♦♥♥ ♥②ss ♠♣ tt ss♣s ♥ t t st ♠♠ ♠♦ st♠t ♣rtt♦♥ ♦tr♥s ♥t t ♥s t ♥ ss ♦r t ♠♠ ♠♦ ♥ ♥ ss ♦r t ♠ ♠♦ ♥ ♥♦r♠ ♥♦s ♦♥ [0, 0.1] s ♦♥ ♦t ①s♦r ♥ ♥ ♦rr t♦ ♠♣r♦ s③t♦♥

❲ ♥♦ sr t st ♦♠♣♦♥♥t ♠♠ ♠♦ ♥ t st♠t♠♦ ss♠s ♦♥t♦♥ ♥♣♥♥ t♥ rs ts ♠♦ s ♦ ♥trsts ♦ ts s♣rst② ♥ t s ♠♦r ♣rs♠♦♥♦s t♥ t ♠ ♠♦ s♥ s♠ ♥♠r ♦ ♠♦s s st♠t s s♦♥ ② t s♠♠r② ♣r♦♣♦s ② κkj♥ ρkj ♥ ♥ ♥ ♣rs♥t ♥ s t rst rs rrtr③ ② ♠♦ts t ♣r♦t② s t rs r ♦♥t♦♥② ♥♣♥♥t κkj ♥ts t ♥♠r ♦ ♠♦ts ♥ ♣r♦t②♣♣r t♥ t ♥♦r♠ strt♦♥ ♦r ①♠♣ t ♠t♥♦♠ strt♦♥ ♦t r s s t♦ ♠♦s ♦r ♦t sss s♦ κkj = 2/3

Page 123: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦♥t♦♥ ♣♥♥② ♠♦s

♦r ②r♦s s ♦rr ♥rss ss

♠♠r② ♦ t ♠♠ ♠♦ t tr sss κkj s s♣② ♥ ♣♥♥ ρkj s s♣② ♥ ♣r♥tss

♠①♠♠ ♦♦ st♠ts ♦ t ♦♠♣♦♥♥t ♣r♠trs r ♣rs♥t② r sr ♦rrs♣♦♥s t♦ ♦ ♦ r ts ♥♦t♥ tt t st♠t ♠♦ ss♠s t ♦♥t♦♥ ♥♣♥♥ ♦r ♦ ♦ rs t ♠♦t② r♦ss♥s r ♦♥ ♠♦ s st♠t ♦r t st♦♥ ♦♠♣♦♥♥t r ♦s ♦r ts ♠♦t② r♦ss♥s s♣② tr ♠t♣r♦t② ♠sss ♦r ♦♠♣♦♥♥t t ♦♠♣♦♥♥t r ♥t ② r♥t♦♦rs s ♠♦t② r♦ss♥s r ♣rs♥t ② rs♥ ♦rr ♦ ♠t♣r♦t② ♠ss

♦t tt t ♠♦ ♦t♦♥s r sr♠♥t s♥ t ♠♦t② rs♣t s ♣r♦t② ♦ rs♣ ♦r ss t ♠♦t② trs♣ ♥ t s ♣r♦t② ♦ rs♣

0.0

0.2

0.4

1 3 2 4

collar.

0.0

0.4

0.8

1.2

3 2

eyebrows.

0.0

0.4

0.8

white black BLACK_white

sub−caudal.

0.0

0.5

1.0

1.5

none few

border.

0.0

0.4

0.8

male

gender.

r ss ♣r♠trs ♦ t ♦♠♣♦♥♥ts ♠♠ ♠♦ st♠t ♦♥ trs t ♦♦r rs♣t② t r② ♦♦r ♦rrs♣♦♥s t♦ t♣r♦t② ♠ss ♦ t ♠♦s ♦r ss rs♣t② t♦ ss

♥② t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ s♠s rst s♥ t ♦♥t♦♥ r♠rs ❱ ♠srs ♣rs♥t ♥ r s♠ ❲ s♦ ♣r♦r♠ ♦♦tstr♣ tst ♦ t ♦ ♥t② ♦ t r♠rs ❱ ② ♥rt♥ s♠♣s ❲ ♦t♥ ♣ ♦ s♦ t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ st

Page 124: Model-based clustering for categorical and mixed data sets

♥②ss ♦ t♦ r t sts

ss

ss

tr① ♦ t r♠rs ❱ ♠srs ♦♠♣t ♦r♥ t♦ t st♠tsss

t ♥♠♠t♦♥s str♥

t ❲ ♥t t♦ str ♣t♥ts ❬❩❪ sr ② ♥r② rs♦rr♥ ♦ ♥s ♠r ♣♥ ♠ r♥ ♣s♥ Ps ♠trt♦♥♣♥s ♥ r♥♥ ♦ rtr r ♥ ② ♦♥ r ♥ tr ♠♦ts t♠♣rtr ♦ t ♣t♥t ♠ T < 37C 37C ≤ T < 38C ♥ 38C ≥ T ❲ ♥♦ tt s♦♠ ♣t♥ts ♦♥ ♦ t ♦♦♥ sss ♦ t r♥r② s②st♠♥♠♠t♦♥ ♦ r♥r② r ♥ ♣rts ♦ r♥ ♣s ♦r♥

①♣r♠♥t ♦♥t♦♥s ❲ s t s♠ ①♣r♠♥t ♦♥t♦♥s s t rsstr♥

sts ♣rs♥ts t s ♦ t rtr♦♥ ♦r ♦t ♠♦s ♥r♥t ♥♠rs ♦ sss ♦r ♥♠r ♦ sss t rtr♦♥ ♦t ♠♠ ♠♦ s ttr t♥ t♦s ♦ t ♠ ♠♦ rtr♠♦r t ♠♠ ♠♦sts tr sss t ♠ ♠♦ sts ♦r sss s ♣♥♦♠♥♦♥ ♥ t♦ t ♦t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ ♦ t ♠ ♠♦

g ♠♠ ♠

❱s ♦ t rtr♦♥ ♦r r♥t ♥♠rs ♦ sss ♥ ♦r t♠♠ ♥ t ♠ ♠♦s ♦ ♥ts t st s ♦ ts rtr♦♥

♦t tt t st♠t strt♦♥s ♦ t ♠ ♥ t ♠♠ ♠♦s r r♥t ♦t♥ ♣rtt♦♥ r s♦ r♥t s♣②s t ♦♥s♦♥♠trs t♥ t st ♠♠ ♠♦ ♥ t ♠ ♠♦s t tr ♥ ♦rsss s ♥s ♦♥sttt r♦♣ s s♣rt r♦♠ t♦tr ♥s ss ♦r t tr ♠♦s t ♦tr ♥s ss♠♠rs♣ tr♠♥ ② t st ♠♦

r s♣②s t ♥s ♦♥ t ♣r♥♣ ♦rrs♣♦♥♥ ♥②ss♠♣ r t st♠t sss r s♣rt

♠♠ ♠♦ t tr sss s t ♦♦♥ r♣rtt♦♥ ♦ t rs♥t♦ ♦s σ = (♠♣ Ps r, , ♠) s s♦♥ ② t s♠

Page 125: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦♥t♦♥ ♣♥♥② ♠♦s

♠♠

♠ ♠ ♠

♠♠

♠ ♠ ♠ ♠

♦♥s♦♥ ♠trs t♥ t st ♠♠ ♠♦ ♥ t ♠ ♠♦st tr ♥ ♦r sss

−0.5 0.0 0.5 1.0

−0.

8−

0.6

−0.

4−

0.2

0.0

0.2

0.4

0.6

Axe 1 of the multiple correspondance analysis

Axe

5 o

f the

mul

tiple

cor

resp

onda

nce

anal

ysis

r ♥s ♦♥ t ♣r♥♣ ♦rrs♣♦♥♥ ♥②ss ♠♣ t tst ♠♠ ♠♦ st♠t ♣rtt♦♥ ♥ ♥♦r♠ ♥♦s ♦♥ [0, 0.1] s ♦♥ ♦t ①s ♦r ♥ ♥ ♦rr t♦ ♠♣r♦ s③t♦♥ ♦♦rs♥ s②♠♦s ♥t t ss ♠♠rs♣

♠r② ρkj ♥ κkj s♣② ♥ t tr sss r ♦♥♥trt ♥ ♠♦t② r♦ss♥s ♦r t ♦ ♦♥ ♥ ♥ ♦♥ ♦t♦♥ t ♣r♦t② ♦s t♦♦♥ ♦r t t♦ ♦tr ♦s

♠♣ ♠ Ps rss ss ss

♠♠r② ♦ t ♠♠ ♠♦ t tr sss κkj s s♣② ♥♣♥ ♥ ρkj s s♣② ♥ ♣r♥tss

♦♦♥ ss ♥tr♣rtt♦♥ s s ♦♥ t ss ♣r♠trs s♣② ②r ♦t tt t rs r♥ ♣s♥ ♥ r♥♥ ♦ rtr r t♠♦st sr♠♥t ♦♥s

♠♦rt② ss r♦♣s ♥s ♥ ♥♦ ♥s ♥ ♥♦ ♠r♣♥

Page 126: Model-based clustering for categorical and mixed data sets

♦♥s♦♥

s♦♥ ss r♦♣s ♥s ♥ ♥♦ ♥s t ♠r ♣♥ tr ss r♦♣s ♥s ♥ ♥s ♥ ♠r ♣♥

rtr♠♦r ts ♥s s♦♠ r ♥ ♠trt♦♥ ♣♥

0.0

0.2

0.4

fiever fiever fiever fiever cold normal cold normalnormalnormal fiever normalyes yes no yes no no yes yes yes yes no yesno yes yes yes no no yes yes yes no no noyes no no yes no no yes no yes no no yes

Tem−Pus−Mic−Bur.

0.0

1.0

2.0

no yes

Nau.

0.0

1.0

2.0

yes no

Lum.

r st♠t ♣r♠trs ♦ t tr♦♠♣♦♥♥t ♠♠ ♠♦ s♣② ②t r♣♦t ♥t♦♥ ♦ t ♣ ♦♦s ♦♦r ♦rrs♣♦♥s t♦ ss r② ♦♦r ♦rrs♣♦♥s t♦ ss ♥ ♣ r② ♦♦r ♦rrs♣♦♥s t♦ ss

♦♥s♦♥

♥ ts ♣tr ♣rs♥t ♥ ♠①tr ♠♦ ♠♠ t♦ str t♦r t ts str♥t s t♦ r① t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ ♥t♦ st② ♣rs♠♦♥♦s s♠♠r② ♦ t strt♦♥ s ♥ ② κkj ♥ ρkj ss ♥ s♠♠r③ ② t ♠♦ ♦t♦♥s s s♦♥ ♦♥ t rs♣♣t♦♥ t ♠♠ ♠♦ ♥ ♦t♣r♦r♠ t ss t♥t ss ♠♦ ♥ t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ s tr t♥s t♦ ts s♣rst②

♦♠♥t♦r ♣r♦♠s ♥♦ ② t ♦ tt♦♥ ♥ ② t st♦♥♦ t ♥♠rs ♦ ♠♦s r ♦ ② tr♦♣♦st♥s ♦rt♠ s♦rt♠ ♥ s s t ♦♠♣tt♦♥ ♦ t ♥trt ♦♠♣tt♦♦ ♥ ♥t② ♣♣r♦①♠t s ts ♣♣r♦ ♥ s t♦st t ♥trt♦♥s ♦ t ♦♥r ♠①tr ♠♦ ♣r ♦

♦r t ♠♦ s r② st♠t t t st s r ♥♠r ♦rs ♦♠ ♦♥str♥ts ♦♥ t ♦ rs r♣rtt♦♥ ♦ s♦ ♦r ♥st♥ t ♥♠r ♦ rs ♥t♦ ♦s ♦ ♠t t♦ tr rs♥♦tr s♦t♦♥ ♦ t♦ st♠t t ♠♦ ② ♦rrr strt②t t s ♥♦♥ tt ts ♠t♦s r s♦♣t♠

Page 127: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ t ♦♥t♦♥ ♣♥♥② ♠♦s

♥② ♠♣♦s t qt② ♦ t r♣rtt♦♥ ♦ t rs ♥t♦ ♦s♦r t sss s ♣r♦♣rt② ♦s s t♦ ♣r♦ t ♥r ♥tt② ♦t ♠♠ ♠♦ s ♦ ①t② s ♦♥tr♥ ② ① ♦ strt♦♥ ♦r ♦♥ ♦ tr② t♦ r① t ssqt② ♦ σ t t ♠♦♥♦♥tt② rs

Page 128: Model-based clustering for categorical and mixed data sets

♣tr

♦ ♦♠♣rs♦♥ ♣r♦r♠ ②

tr ♣s

s ♣tr ♠ s t♦ strt t ♣sstrt ♥ ♦♦s rs♣t② ♣r♦r♠t ♥r♥ ♦ t ♠ ♥ t ♠♠ ♠♦s♥ ♦rr t♦ ♠ ♠♦♥strt♦♥ ♦ ♦t ♣s s t♠ t♦ ♣r♦r♠ t str ♥②ss ♦ t r♥♥♥①♠♣ ♣rs♥t ② ♦ ♣tr ❲ r♠♥ tt ts t st s♣②s t t♦♥ s♦♥♦r r♦s ♦ ♥t ①r②s tt ♠② s♦ ♥♣♥t rs ♣r♦r♠ ② ♥tsts♦t tt ts ♣tr ♥ s♦ s s tt♦r ♦♦t ♣s ♥ t ♣r♦s ♣rs♥tt♦♥ ♦ tr♠♥ ♥t♦♥s ♥ ♠♥② sr♣ts ♦♥ t♦ ♣r♦r♠ tstr ♥②ss

♥♠s r q t s♦♠♥♠s r ♠♦r q t♥ ♦trs

♦r r ♥♠ r♠

strt

strt ♦r

Prs♥tt♦♥ ♣ strt ♣r♦r♠s t str♥ ♦ t♦rt ♦r♥ t♦ t ♠ ♠♦ ts ♠♥ ♥t♦♥s r ♠♣♠♥t ♥ ❲ r♠♥ tt ♥ ts ♠♦ rs r r♦♣ ♥t♦ ♦♥t♦♥② ♥♣♥♥t ♦s ♥ ♦rr t♦ ♦♥sr t ♠♥ ♥trss ♦rrt♦♥s ♥trss♣♥♥② t♥ rs r♦♣ ♥s t s♠ ♦ s t♥ ♥t♦ ♦♥t② ♠①♥ t♦ ①tr♠ strt♦♥s r rs♣t② ♥♣♥♥ ♥ ♠①♠♠ ♣♥♥②

Page 129: Model-based clustering for categorical and mixed data sets

♣tr ♦ ♦♠♣rs♦♥ ♣r♦r♠ ② tr ♣s

♦♥♦ strt ♣ s rr♥t② ♦♥ ♦r t t ♦♦♥ r tt♣sr♦rr♣r♦t♦rr♦♣❴ ♥stt♦♥ ♥t ♦♥ ♦ strt ♥ ♣r♦r♠ ② s♥ t ♦♦♥ sr♣ts

♥st ♦♠♠♥> ♥st♣sstrt

r♣♦stt♣♦r♣r♦t♦r

strt sr♣t strt ♥stt♦♥

strt ♦♥> rqrstrt

strt sr♣t strt ♦♥

st♠t♦♥ ♣r♠tr st♠t♦♥ ② ♠①♠♠ ♦♦ s ♣r♦r♠ ♠ ♦rt♠ s ♦rt♠ s ♦r t ♠♦ st♦♥ ♦s t♦♠♥t♦r ♣r♦♠s ♥ ② t ♦ strtr sr

♥ ♥t♦♥s

♥t♦♥s ♦♠♣♦s t strt ♣ ♥ ♥t♦♥ ♣r♦r♠s tstr ♥②ss ♦ t t ts t♥♥ ♣r♠trs ♥ s♣ ② t sr ②♥ s♣ ♥t♦♥ tr st ♥t♦♥s r ♠♣♠♥t ♥ ♦rr t♦r♥② ♣rs♥t t ♣r♠trs ② ♣r♦♥ ♥♠r ♦r r♣ s♠♠rs

str♥ ♥t♦♥ str ♥②ss ♥ ♣r♦r♠ t t ♥t♦♥strt t♥ ♦r r♠♥ts

> strtt ♥❴str

♠♦ strt② strt②tt

strt sr♣t str♥ ♥t♦♥

s ♥t♦♥ s t♦ ♠♥t♦r② r♠♥ts t r♠ t t♦ str ♦s t ♦♠♥s r ♥♦♥③r♦ ♥trs ♦r

t♦rs ♥ ♥tr t♦r ♥❴str s♣②♥ t ♥♠r ♦ sss

t s r t♥ ♦r t r♠♥ts ♠♦ ♥ strt② r♠♥t ♠♦ s t♦r ♥ t ♠♦t② ♥♠r ♦r r r♠♥t strt② s ♥ ♥st♥ ♦ t strt②t ss ♦♥

t♥s t st♠♥ts ♥♣ts ♣r♠trs rt t♦ t st♠t♦♥ ♦rt♠s

Page 130: Model-based clustering for categorical and mixed data sets

strt

♥t♦♥ strt rtr♥s ♥ ♥st♥ ♦ t stt ss ♦♥t♥s t ♦t♣ts

t♥♥ ♥t♦♥ st♠♥ts ♣r♠trs ♦ t st♠t♦♥ ♦rt♠s♦♥t♥ ♥ t strt②t ss ♥ s♣ ② t ♥t♦♥ strt②t

t♥ ♦r r♠♥ts

> strt②tt ♥❴♥t st♦♣❴rtr♦♥

♥♦t ♣rtt♦♥ ♣rtt♦♥tt

strt sr♣t t♥♥ ♥t♦♥

♥♣t t ♠tr① t s ♠♥t♦r② ♥ t tr ♦trs ♥♣t ♣r♠trs♦ t♦ t♥ t ♦rt♠s

r♠♥t ♥❴♥t sts t ♥♠r ♦ t♠s r ♠♠ ♥ sstrt ② t ♠♠ ♥s r ♥t③

r♠♥t st♦♣❴rtr♦♥ s t ♥tr ♦rrs♣♦♥♥ t♦ t ♥♠r♦ sss trt♦♥s ♦ t ♠♠ ♥ r ♥♦ ttr ♠♦ s ♥t♥ t ♦rt♠ s st♦♣♣ ② t t ts t s ♦ 20× d

r♠♥t ♣rtt♦♥ s t ♥t ♦ t r♣rtt♦♥ ♦ t rs♥t♦ ♦s σ[0] ② t t s q t♦ t ♣rtt♦♥ ♣r♦ ② t ♠♥♠③♥ t ♦ ♥♠r t♦t ♦ ♦♥sst♥ ♦ ♠♦r t♥♦r rs

r t♦♦ ♥t♦♥s strt ♣ s♦ ♣r♦s t♦♦ ♥t♦♥s s♠♠r② s♠♠r②❴♣♥♥s ♥ ♣♦t rs♣t② t♦ s♠♠r③ rsts t♦ ♣rs♥t t ♠♥ ♦♥t♦♥ ♣♥♥s ♥ t♦ s③ t ♣r♠trs

strt t♦ str t ♥tsts t st

str♥ t ♠ ❲ ♥♦ ♣rs♥t t rsts ♦ t ♠ ♠♦ ♦t♥② t ♣ strt ② s♥ t ♦♦♥ sr♣t

Page 131: Model-based clustering for categorical and mixed data sets

♣tr ♦ ♦♠♣rs♦♥ ♣r♦r♠ ② tr ♣s

t st ♦♥> t♥tst

♥t♦♥ ♦ t t♥♥ ♣r♠trs ♦r t st♠t♦♥ ♦rt♠> st strt②t♥tst ♥❴♥t st♦♣❴rtr♦♥

st♠t♦♥ ♦ t ♦♠♣♦♥♥t ♠ ♠♦> rs strt♥tst ♠♦ r♣ strt②

st

strt sr♣t ♥tsts t st str♥

♦ st♦♥ rtr♦♥ sts t♦ sss t ♦ t ♠s tt t ♠ ♠♦ ttr ts t t t♥ t ♠♦ ♣rs♥t ♥❬❪ s♥ ts rtr♦♥ s rtr♦♥ s ♦r t ♠♥ t ♠ ♠♦s r s♣② ♥ ❲ ♥t t ♦♠♣t♥ t♠♥ s♦♥s ♦t♥ t ♣r♦ss♦r ♥t ♦r t♦ st♠t t ♠♠♦ r ♠♠ ♥s r strt t st♦♣♣♥ rs qmax = 100 t ♠ ♠♦ ♥s ss t♥ s t t ♣ ①♠♦ ❬+❪

g ♠ ♠

t♠ s

rtr♦♥ s ♦r t ♠ ♥ t ♠ ♠♦s ♦r♥ t♦r♥t ♥♠rs ♦ sss ♦r t ♥tstr② t st st s r ♥ ♦

❲ ♥♦t tt t ♠ ♠♦ ♦t♥s ttr s ♦r t rtr♦♥ t♥t ♠ ♠♦ ♥ g = 1, 2 ❲♥ t ♥♠r ♦ sss s rr g ≥ 3 t st♠ ♠♦ ss♠s t ♦♥t♦♥ ♥♣♥♥ t♥ rs

♦♠♣rs♦♥ ♦ t rsts rtr♦♥ sts t♦ sss ♦r t ♠♠♦ s rst s ♦r♥t t s♣tt♥ ♦ t tt t♥ t s♦♥ ♥t r♦s ♦♥s rtr♠♦r t t♦ ♠♥ rtrsts ♦ t ♦♥r ♠①tr♠♦ ♠♣♦s ♥ ❬❪ r t♦♠t② tt ② t ♠♦ ♠♣♦rt♥ ♦t t♦ ♠♦t② r♦ss♥s r t ♥tsts t s♠ ♥♦ss ♥ ♣♥♥② t♥ t ♥♦ss ♦ t ♥tsts ♥ s t st♠t♠♦ s ♦r♥t t t ♠♣♦s ♠♦ ♣rs♥t ♥ ❬❪ ♥♦ ♥♦r♠t♦♥s ♥ ♣r♦r

Page 132: Model-based clustering for categorical and mixed data sets

strt

st ♠♦ ♥tr♣rtt♦♥ ♠♦ ♥tr♣rtt♦♥ s tt ② t♦ t♦♦s♥t♦♥s ♥t♦♥ s♠♠r② ♣r♦s ♥r ♦r ♦ t ♠♦ ♥♦r♠t♦♥ rtr ♣r♦♣♦rt♦♥s ♦s ♦ rs ♥ ♥trss ♣♥♥sρkb

♥t♦♥ ♣r♦♥ ♠♦ ♦r> s♠♠r②rs

♠r ♦ sss ♦♦♦ Pr♦♣♦rt♦♥s ♦s r♣rtt♦♥ ♦ t rs ♦r t ss

❱rs ♦♦

♦s r♣rtt♦♥ ♦ t rs ♦r t ss ❱rs ♦

♦ ♦

strt sr♣t ♦ ♦r

♥t♦♥ s♠♠r②❴♣♥♥s ♦ss ♦♥ t ♥trss ♣♥♥② ♣r♠trs

♥t♦♥ ♣r♦♥ s♠♠r② ♦ t ♥trss ♣♥♥s> s♠♠r②❴♣♥♥srs

♦s r♣rtt♦♥ ♦ t rs ♦r t ss

♦ ♦♥t♥s t rs t ♦

s♦♥ s♦♥ s♦♥ s♦♥ s♦♥ r♦s r♦s r♦s r♦s r♦s♦s r♣rtt♦♥ ♦ t rs ♦r t ss

♦ ♦♥t♥s t rs t ♦

s♦♥ r♦s r♦s s♦♥

♦ ♦♥t♥s t rs t ♦

strt sr♣t ♠♠r② ♦ t ♥trss ♣♥♥s

Page 133: Model-based clustering for categorical and mixed data sets

♣tr ♦ ♦♠♣rs♦♥ ♣r♦r♠ ② tr ♣s

♦r♥ t♦ t ♣r♦s ♦t♣ts t tt ♠♦ ♥ ♥tr♣rt s ♦♦s

♠♦rt② ss π1 = 0.86 ♠♥② trs t s♦♥ tt r s str♦♥ ♣♥♥② t♥ t ♥♦ss σ1 = (1, 2, 3, 4, 5) ♥ρ11 = 0.35 ♣♥♥② strtr ♦ t ♠①♠♠ ♣♥♥② strt♦♥ ♥ts ♥ ♦r ♦♥trt♦♥ ♦ ♦t ♠♦t② ♥trt♦♥s rt ♥tsts t s♠ ♥♦ss s♣② ♥ t② ♠ tt tt♦♦t s s♦♥ τ ❴s♦♥

11 = 0.95 ♥ τ ❴r♦s11 = 0.05

♠♥♦rt② ss π2 = 0.14 r♦♣s ♣r♥♣② t r♦s tt r s ♣♥♥② t♥ t ♥tsts ♥ ♣r♦s ♦♣♣♦st ♥♦ss t ♥♦ss ♦ t ♦tr ♦♥s r ♥♣♥♥t ♥ t ss σ2 =(3, 4, 1, 2, 5) ρ21 = 0.25 ♥ ρ22 = 0

st ♠♦ s③t♦♥ ♥② t ♥t♦♥ ♣♦t ♣r♦s r♣ s♠♠r② ♦ t ♣r♠trs

♥t♦♥ ♣r♦♥ t r♣ s♠♠r② ♥ ② r > ♣♦trs

strt sr♣t r♣ s♠♠r② ♦ t ♣r♠trs

♥ ♦r♥ts t st♠t sss r r♣rs♥t t rs♣t t♦ tr ♣r♦♣♦rt♦♥ ♥ rs♥ ♦rr ♦t tt tr ♦rrs♣♦♥♥ r ♣♥s ♦♥ tr♣r♦♣♦rt♦♥ ♠t ♣r♦♣♦rt♦♥s r ♥t ♦♥ t t s ♥ ssstr ♥t♦♥s r ♥ rst ♦♥ s t ♥trrs ♦rrt♦♥s ρkb♦r t ♦s ♦ t ss ♦rr ② tr str♥t ♦ ♦rrt♦♥ ♥ rs♥♦rr s♦♥ ♦♥ s t ♥trrs ♦rrt♦♥s τ kb ♦r ♦ r♥♦r♥ t♦ t str♥t ♦ tr ♣♥♥s ♥ rs♥ ♦rr tr ♦♥s t rs r♣rtt♦♥ ♣r ♦s ♥ts tt t r sss♥ ♥t♦ t ♦ ♥ t ♥ts tt ♦♥t♦♥② ♦♥ ts sst r s ♥♣♥♥t ♦ t rs ♦ ts ♦ ♦r ①♠♣ ts rs♦s tt t rst ss s ♣r♦♣♦rt♦♥ ♦ 0.86 ♥ tt t rs rss♥ ♥t♦ t s♠ ♦

♦♦s

♦♦s ♦r

Prs♥tt♦♥ ♣ ♦♦s ♣r♦r♠s t str♥ ♦ t♦rt ♦r♥ t♦ t ♠♠ ♠♦ ❲ r♠♥ tt ♥ ts ♠♦ rs rr♦♣ ♥t♦ ♦♥t♦♥② ♥♣♥♥t ♦s q t♥ sss ♥ tt ♦ ♦♦s ♠t♥♦♠ strt♦♥ ♣r ♠♦s t ♥t♦♥s ♦ ts ♣r ♠♣♠♥t ♥ ♦ t② s♦ ♠♣♠♥t ♥ ♥ ♦rr t♦ ♥rst ♦♠♣tt♦♥ s♣

Page 134: Model-based clustering for categorical and mixed data sets

♦♦s

1 0.75 0.5 0.25 00 0.25 0.5 0.75 1ρkb τkb

0

0.86

1

Class 1

de1

de2

de3

de4

de5

Class 2

de1

de2

de3

de4

de5

de1

de2

de3

de4

de5

r ♠♠r② ♦ t st ♠ ♦r♥ t♦ ♦r t ♥tsts t st

♦♥♦ ♦♦s ♣ s rr♥t② ♦♥ ♦r t t ♦♦♥ r tt♣sr♦rr♣r♦t♦rr♦♣❴ ♥stt♦♥ ♥t ♦♥ ♦ ♦♦s ♥ ♣r♦r♠ ② s♥ t ♦♦♥ sr♣ts

♥st ♦♠♠♥> ♥st♣s♦♦s

r♣♦stt♣♦r♣r♦t♦r

♦♦s sr♣t ♦♦s ♥stt♦♥

♦♦s ♦♥> rqr♦♦s

♦♦s sr♣t ♦♦s ♦♥

st♠t♦♥ ♣r♠tr st♠t♦♥ ② ♠①♠♠ ♦♦ s ♣r♦r♠ ②♥ ♦rt♠ s ♦rt♠ s s ♦r ♠♦ st♦♥ t♦ ♦ ♦♠♥t♦r ♣r♦♠s ♥♦ ② t ♦ strtr sr ♦t tt t s♦rt♠ ss ♥ ♥t ♣♣r♦①♠t♦♥ ♦ t ♥trt ♦♠♣tt ♦♦

♥ ♥t♦♥s

♦r ♥t♦♥s ♦♠♣♦s t ♦♦s ♣ ♦♥ ♥t♦♥ ♣r♦r♠s t str♥②ss ♥ tr ♥t♦♥s ♣ ♦r t rst ♥tr♣rtt♦♥

Page 135: Model-based clustering for categorical and mixed data sets

♣tr ♦ ♦♠♣rs♦♥ ♣r♦r♠ ② tr ♣s

str♥ ♥t♦♥ str ♥②ss ♥ ♣r♦r♠ t t ♥t♦♥♦♦sstr t♥ s♥ r♠♥ts

> ♦♦sstr① s❴♥t s❴tr

s❴ ❴♥t ❴t♦

♦♦s sr♣t ♥tsts t st str♥

s ♥t♦♥ s t♦ ♠♥t♦r② r♠♥ts t r♠ ① t♦ ♥②③ ♦s ♦♠♥s r t♦rs ♥ ♥tr s stt♥ t ♥♠r ♦ sss

t s r t♥ ♦r t t♥♥ r♠♥ts ♥♠r ♦ ♠♠ ♥s ♣r♦r♠ t♦ st t st ♠♦ s st ② t

r♠♥t s❴♥t t s ♥♠r ♦ trt♦♥s ♦ t s s♠♣r s st ② t r♠♥t s❴tr

t s ♥♠r ♦ trt♦♥s ♦ t r♥♥ ♦ t s s♠♣r s st ② t

r♠♥t s❴ t s ♥♠r ♦ r♥t ♥t③t♦♥s ♦ t ♠ ♦rt♠ st♠t♥ t

♠ ♦r t st ♠♦ ♦r♥ t♦ t s s♠♣r s st ② t r♠♥t❴♥t t s

♠ ♦rt♠ s st♦♣♣ ♥ t ♥rs ♦ t ♦♦ s s♠rt♥ ❴t♦ t s

r t♦♦ ♥t♦♥s ♦♦s ♣ s♦ ♣r♦s t♦♦ ♥t♦♥s s♠♠r② r♣♦t ♥ ♣♦t rs♣t② s♠♠r② ♦ t ♠♦ r♣ s♠♠r② ♦ t ♣r♠trs ♥ sttr♣♦t ♦ t ♥s ♥ t♠t♣ ♦rrs♣♦♥♥ ♠♣

♦♦s t♦ str t ♥tsts t st

str♥ t ♠♠ ❲ ♥♦ s♣② t rsts ♦ t ♠♠ ♠♦ st♠tt t ♣ ♦♦s ② s♥ t ♦♦♥ sr♣t

> rs ♦♦sstr♥tst s❴♥t

s❴tr ❴♥t

♦♦s sr♣t ♥tsts t st str♥

♦ st♦♥ s ♦ t rtr♦♥ ♦t♥ ② t tr ♠♦s♠ ♠ ♥ ♠♠ r ♣rs♥t ♥

♠♦ tt♥ t st t t s t ♦♠♣♦♥♥t ♠ ♠♦ ♠♠♠♦ ts t t ttr t♥ t ♠ ♠♦ ♦r ♥♠r ♦ sss s♠r t♥

Page 136: Model-based clustering for categorical and mixed data sets

♦♦s

g ♠ ♠ ♠♠

rtr♦♥ s ♦r t ♠ t ♠ ♥ t ♠♠ ♠♦s ♦r♥ t♦ r♥t ♥♠rs ♦ sss ♦r t ♥tstr② t st st s r ♥♦

tr ♦t tt ♥ t ss ♥♠r s ♣♣r ♦r q t♦ tr ♦t ♠ ♥♠♠ ♠♦s r q♥t t♦ t ♠ ♠♦

♣♦ss rs♦♥ ①♣♥♥ t ♣♦♦r ♣r♦r♠♥ ♦ t ♠♠ ♠♦ ♦ ts ♦♥str♥t ♦ t qt② t♥ ss ♦ t r♣rtt♦♥ ♦ t rs ♥t♦♦s ❲ r♠♥ tt ts ss♠♣t♦♥ s ♥♦t ♠ ② t ♠ ♠♦

♥ ♦rr t♦ strt t t♦♦s ♥t♦♥s ♦ ♦♦s ♥♦ ♥②③ t ♦♠♣♦♥♥t ♠♠ ♠♦

♦♠♣♦♥♥t ♠♠ ♥tr♣rtt♦♥ ♠♦ ♥tr♣rtt♦♥ ♦ t ♠♠♠♦s tt ② tr t♦♦s ♥t♦♥ ♥t♦♥ s♠♠r② ♣r♦s ♥r♦r ♦ t ♠♦ ♥♦r♠t♦♥ rtr ♥♠rs ♦ ♠♦s τkb κkb

Page 137: Model-based clustering for categorical and mixed data sets

♣tr ♦ ♦♠♣rs♦♥ ♣r♦r♠ ② tr ♣s

♥t♦♥ ♣r♦♥ ♠♦ ♦r> s♠♠r②rs

♠r ♦ rs ♠r ♦ ♥s ♠r ♦ ♠♦ts ss ♥♠r ♦♦♦

♦ ♥♠r

ss ss

♥①

ss ss ♣♣ ♥①

ss ss

♦♦s sr♣t ♥tsts t st str♥

♥t♦♥ r♣♦t ♣r♦s r♣ s♠♠r② ♦ t ♣r♠trs ♥ t ♣♦ts r♣♦t rt♥ t ♣r♦t② ♦ t ♠♦s ♣r ss ♦r ♦② ♦rr♥ t ♠♦t② r♦ss♥s ♦r♥ t♦ tr ♣♦str♦r ♣r♦t②

r♣♦t ♦ t ♣r♠trs ♣rs♥t ② r > r♣♦trs

♦♦s sr♣t ♥tsts t st str♥

♠♦rt② ss s♣② ♥ r② s ♠♥② ♦♠♣♦s t t s♦♥ ♥♦ss s♦♥ ss s♣② ♥ s ♦♠♣♦s t tt ♥♦s sr♦s ② s♦♠ ♥tsts s♣② t t ♦t tt t ♥tst ♠♥② ♥♦ss t tt s s♦♥ s♥ ts ♦rrs♣♦♥♥ r s ♠♦ ♥ ts ♦t♦♥♦r ♦t sss

♦♠♣♦♥♥t ♠♠ s③t♦♥ ♥t♦♥ ♣♦t ♣r♦s sttr♣♦t♦ t ♥s ② ♥t♥ tr ss ♠♠rs♣ ♦r♥ t♦ t ♠♣ r♥ ♦rrs♣♦♥♥ ♥②ss ♠♣ r t ①s r ♦s♥ ② t sr

Page 138: Model-based clustering for categorical and mixed data sets

♦♦s

0.0

0.4

0.8

sound sound carious sound sound carioussound carious carious carious sound soundsound sound carious carious carious sound

de1−de2−de3.

0.0

0.4

0.8

1.2

sound

de4.

0.0

0.4

0.8

carious sound

de5.

r ♠♠r② ♦ t ♠♠ ♣r♠trs

ttr♣♦t ♦ t ♥s ♣rs♥t ② r > ♣♦trs

♦♦s sr♣t ♥tsts t st str♥

Page 139: Model-based clustering for categorical and mixed data sets

♣tr ♦ ♦♠♣rs♦♥ ♣r♦r♠ ② tr ♣s

−0.5 0.0 0.5 1.0 1.5 2.0 2.5

−0.

50.

00.

51.

01.

5

Axe 1 of the multiple correspondence analysis

Axe

2 o

f the

mul

tiple

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anal

ysis

r ttr♣♦t ♥ t rst ♦rrs♣♦♥♥ ♥②ss ♠♣ ♥s t t♦ t ♠♦rt② ss r s♣② ② r tr♥s t♦s t t♦t ♠♥♦rt② ss r s♣② ② rs ♥ ♥♦r♠ ♥♦s ♦♥ ❬ ❪s ♥ t♦♠t② ♦♥ ♦t ①s ♦r ♥ ♥ ♦rr t♦ ♠♣r♦s③t♦♥

Page 140: Model-based clustering for categorical and mixed data sets

♦♥s♦♥ ♦ Prt

❲ s♥ tt t ss t♥t ss ♠♦ ♦s t♦ t t s♠t sts t♥s t♦ ts s♣rst② ♥ ② ts ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ ♦r s t sts ♠♦r ♦♠♣① ♠♦s t♥ ♥t♦ ♦♥t t ♥trss♣♥♥s r rr♥t s♥ t ♥♦r♠t♦♥ ♦ ♦♥t♦♥ ♣♥♥② s ♥♦t♣rs♥t

❲♥ t ♥♠r ♦ ♥s s s♥t② r ♦r♥ t♦ t ♥♠r ♦rs t t♥t ss ♠♦ ♥ s ♥ ts ♦♥t♦♥ ♥♣♥♥ss♠♣t♦♥ s ♦t ❲ ♣rs♥t tr ♠♥ ♠♦s ♦ t ♦r♣②r①♥ ts ss♠♣t♦♥ t t② r ♥ t r♥t ts ♠♦ st♦♥ ♥stt② ♦r ♥ ♥tr♣rtt♦♥ ♦ sss s ♣r♦r♠ tr♦♦t♥♦tr t♥t r

❲ s♦ ♣r♦♣♦s t♦ ♠①tr ♠♦s r s♣ rs♦♥s ♦ t♦♥r ♠①tr ♠♦ ② ♦♥sr t ♠♥ ♥trss ♣♥♥s t♥st♦ ♦♠♣♦♥♥t strt♦♥ ♣r ♥♣♥♥t ♦s r ♠♥ str♥t s tt♦t ♠♦s ♥ s♠♠r③ ② ♠♥♥ ♣r♠trs ♥ t ♠♠♦ ♣r♦s ♦♥ ♦♥t ♥ ♦♥ ♣♥♥② rt♦♥s♣ ② ♦s ♦ rs t ♠♠ ♠♦ ♣r♦s rtrst ♠♦s ♥ t♦ ♥t♦rs♦ t ♣♥♥② str♥t ♣r ♦ ♦t tt ♦t ♠♦s ♥ ♦♥sr ♥trt♦♥s ♠♦♥ ♠♦r t♥ t♦ rs t s ♦♥s t ♥t♦ ♦♥t t♥trt♦♥s ♦ ♦rr ♦♥ ♦r t♦

s ♠♦s ♦ t ♦♥r ♠①tr ♠② ♦t ♣r♦♣♦s ♠♦s r ♥t ♦♠♣① ♥ ♦r t ♠♦ st♦♥ s t② s ♦ s♥ t♥♠r ♦ ♦♠♣t♥ ♠♦s ♥ rtr♠♦r t ♥♦r♠t♦♥ rtrr ♥r② s②♠♣t♦t s♦ t② r ♥ ♥ t ♥♠r ♦ ♠♦s s r♦r♥ t♦ t s♠♣ s③ ♦r ♥st♥ t rtr♦♥ s s t♦ st t♠♦ ♥♠r ❲ ♣r♦♣♦s ♠♠ ♦rt♠ ♣r♦r♠♥ r♥♦♠ ♠♦♥ t ♠♦s ♥ ♦rr t♦ r ts r ♦r t ♦♠♣tt♦♥t♠ ♥rss t t s③ ♦ t ♠♦ s♣ s ♣♥♦♠♥♦♥ s str♦♥♦st t♦ t ♥②ss ② t ♣r♦♣♦s ♠♦s ♦ t sts t r ♥♠r♦ rs s t ♠ ♠♦ s rsr ♦r t str ♥②ss ♦ t stst rs ♠♠ ♠♦ s ss ♦♠♣① ♥ ts ♠♦ st♦♥ ♦s ♥♦trqr ♥② ♣r♠tr st♠t ♦ t ♠♠ ♠♦ ♥ str ♠♦r ♦♠♣① tsts ♦r ♦r s t♦ s ts ♠♦s ♦♥ t sts ♥ t ♠♦st rs ♥ ♥ t ♥♠r ♦ rs s r tr r t♦♦ ♠♥② ♠♦s♥ ♦♠♣tt♦♥ ♦ t s ♦rt♠ rqrs t♦♦ ♠♥② trt♦♥s t♦ s♠♣♦r♥ t♦ ts stt♦♥r② strt♦♥ ♥ s s ♣r♠t ♣♣r♦ ♦♦♥ssts ♥ t st♠t♦♥ ♦ ♦♦ ♠♦ t ♥♦t t st ♦♥ s t ♠♦

Page 141: Model-based clustering for categorical and mixed data sets

♣tr

st♦♥ ♦ ♣r♦r♠ ② tr♠♥st t s♦♣t♠ ♣♣r♦ t♦rr ♠t♦

♥② t ♣♣r♦s ♣r♦r♠♥ t ♠♦ st♦♥ ♦t♥ rqr t♦ ♥rt ♣r♠trs ♦r ♥t ♠♦ t ♦♥② ♥tr♣rt ♦♥s r t♦srt t♦ t st ♠♦ r ♦ ♦♥ssts ♥ ♣r♦r♠♥ t ♠♦st♦♥ t♦t ♥♥ t ♣r♠trs ♦ t ♠♦ ♥ts ♥ tr t♦ ♥rt ♣r♠trs ♦♥② ♦r t st ♠♦ ♥ ♠♦s ♥ ts ♣r♦♣rt② s♠♣② t ♥ ♦ t ♠♦ st♦♥

Page 142: Model-based clustering for categorical and mixed data sets

Prt

♦s str♥ ♦r ♠①

t

Page 143: Model-based clustering for categorical and mixed data sets
Page 144: Model-based clustering for categorical and mixed data sets

s ♣rt ♦t t♦ t str ♥②ss ♦ ♠① ts s♣t ♥ tr ♣trs rst ♦♥ ♣rs♥ts ♥ ♦r ♦ t str♥ ♣♣r♦ s♣ t♦ t ♠① t sts ❲ ♠♥② ♦s♦♥ t t♦ ♠♥ ♠①tr ♠♦s r① t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ ♥ ♥ ♣rt ♦t ♦ ♠trt strt♦♥s ♦r ♠① t s♦♥ ♣tr ♣rs♥ts ♠①tr ♠♦ t♦ str ♠① t sts t ♦♥t♥♦s ♥ t♦r rs s ♠♦ rs r♦♠ t ♠t t♥t ss♠♦ ♦♣ ♦r t t♦r t ♥②ss tr ♣tr ♣rs♥ts ♦♥ ♦ t ♠♥ ♦♥trt♦♥♦ ts tss t ♦♥ssts ♥ t ♠①tr ♠♦ ♦ ss♥ ♦♣s ♦s t♦ str t sts t ♥②♥ ♦ rs ♠tt♥ ♠t strt♦♥♥t♦♥ s rsts r ♣rt ♦ s♠tt rt

❯♥ ♦s ♥st ♣s ♦t♠♥ ① rs

♥ tr♦s r r

♦♥ ♠s sà ♣rtr ♦♠♥

étt ♦t♠té♣♥ r♦♦t ❱♦②s ♥

sr

Page 145: Model-based clustering for categorical and mixed data sets
Page 146: Model-based clustering for categorical and mixed data sets

♦ ♦♥t♥ts

str ♥②ss ♦ ♠① t sts stt ♦ t rt

♥ ♦ str ♥②ss ♦r ♠① t r ♦ s♠♣ ♠t♦s t♦ str ♠① t ①tr ♦ ♦t♦♥ ♠♦s ♥ ts ①t♥s♦♥ ♣r ♦s ❯♥r♥ ss♥ ♠①tr ♠♦ ♦♥s♦♥

♦s str♥ ♦ ss♥ ♥ ♦st strt♦♥s

♥tr♦t♦♥ ①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s ①♠♠ ♦♦ st♠t♦♥ ♥ ♦rt♠ ♦ st♦♥ ♦rt♠ ♠r ①♣r♠♥ts ♦♥ s♠t t sts ♥②ss ♦ t♦ r t sts ♦♥s♦♥

♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t

♥tr♦t♦♥ ①tr ♠♦ ♦ ss♥ ♦♣s ②s♥ ♥r♥ tr♦♣♦st♥s s♠♣r ♠r ①♣r♠♥ts ♦♥ s♠t t sts ♥②ss ♦ tr r t sts ♦♥s♦♥

♦♥s♦♥ ♦ Prt

Page 147: Model-based clustering for categorical and mixed data sets
Page 148: Model-based clustering for categorical and mixed data sets

♣tr

str ♥②ss ♦ ♠① t sts

stt ♦ t rt

♣r♣♦s ♦ ts ♣tr s t♦ ♣rs♥t t ♠♥ ♣♣r♦s t♦ str ♠① t stsrst② ♠♣s③ t s♣t② ♦ t ♠① t♦r t str ♥②ss♦♥② ♣rs♥t s♦♠ ♥ ♣♣r♦s t♦ ♣r♦r♠t str ♥②ss ♦ s t♥② t t t♦ ♠♦st r♥t ♣♣r♦s t♦str ♠① t sts

é♥étq ♥ ♥♦èrs r♦♠♦s♦♠s ♥s t♠♦s♣èr

s t①s ♣♦r s ①st ♠♦♥ t♣s ♦♥t

♦r ésr ♥t ♥♦s ♣♦rtr

♥ ♦ str ♥②ss ♦r ♠① t

♥tr♦t♦♥ ♦②s ♠♥② t sts r ♦t♥ ♦♠♣♦s t ♠① rs r♥t ♥s ♦ rs ♥ t t st ♦ t s ss♥t t♦ t♦str s t sts t② ♥r♥t t♦ t str ♥②ss ♦ ♠① t♣r♦r♠ ② ♠①tr ♠♦s s t ♦ ♠trt strt♦♥ ♦r s t♥ t ss♥ rs♣t② t P♦ss♦♥ ♥ t ♠t♥♦♠ strt♦♥s r rr♥ t♦ str ♦♥t♥♦s rs♣t② ♥tr ♥ t♦r ttr s ♥♦ rr♥ strt♦♥ ♦r ♠① t ♦ t sst ♣♣r♦ ♦♥ssts♥ ss♠♥ t ♦♥t♦♥ ♥♣♥♥ t♥ t rs ♥ ♥ st♥ss ♦♥♠♥s♦♥ ♠r♥ strt♦♥s ♦r ♦♠♣♦♥♥t ♦r ts♣♣r♦ ♥ t♦ ss ♦r♦r s♦♠ ♠♦s ♣♣r♦ t strt♦♥ ♦♥trss ♦rrt ♠① t t t② r ♥♦t s② ♠♥♥ ♥ t♦♥♠♥s♦♥ ♠r♥s ♦ ♦♠♣♦♥♥t ♦ ♥♦t ♦♦ ss strt♦♥s st ♠①tr ♠♦s r s t♦ str ts ♦t ♣♣rs t♦ r ♦r ss♥ t ♣r♦s ♠♥♥ ♠♦s

Page 149: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss ♦ ♠① t sts stt ♦ t rt

♦ r ♦ts s ♣tr ♣r♦♣♦ss ♥ ♦r ♦ t str♥ ♣♣r♦s ♦r ♠① t t ♣ts t t ♦♥ t ♠♣♦rt♥ t♦ ♣r♦ ♠trt♠①tr ♠♦s rs♣t ♦t ♦♦♥ ♦ts

♦ ♣r♦ ss ♦♥♠♥s♦♥ ♠r♥ strt♦♥s ♦r ♦♠♣♦♥♥t

♦ ♠♦③ t ♥trss ♣♥♥s

s t♦ ♦ts ♠ t♦ s♠♣② t ♠♦ ♥tr♣rtt♦♥ ♥ t ♣rtt♦♥r s ♥ s r♠♦r ♥ t ♦♥♠♥s♦♥ ♠r♥s ♦ t ♦♠♣♦♥♥tsr ss ♦r♦r ts ss ♥tr♣rtt♦♥ s ♠♦r ♣rs ♥ t ♥trss♣♥♥s r ♠♦③ s t ♠①tr ♠♦s ♣rs♥t ♥ t ♦♦♥♣trs ♠ t rs♣t♥ ♦t ♦ts

t r♦♦t ts ♣rt ♦♥sr t ert t♦r ♦ ♠① rs ♥♦t ② xi = (x1i , . . . , x

ci ,x

c+1i , . . . ,xei ) ❲ ♥♦t ② x

i = (x1i , . . . , xci)

ts sst ♦ t c ♦♥t♥♦s rs ♥ t s♠ ② ♥♦t ② x

i =(xc+1

i , . . . ,xei ) ts sst ♦ t d srt rs r c + d = e ♦t ttt tr♠ srt s♣ ♦r ♣rs♥t ♠♦ ❲ ♥♦t ② m t♥♠r ♦ ♠♦t② r♦ss♥s ♦ t st ♦ t t♦r rs ♦ x

i

trtr ♦ ts ♣tr t♦♥ rst② ♣rs♥ts tr ♥ ♠t♦s r ♥♦t r♥t s♥ t② ♦ ♥♦t ♦♥sr r ♥ ts ♥t s♣ ♦♥②ts st♦♥ ♦r♠ts t♦ ①t♥s♦♥s ♦ ss ♠t♦s t♦ ♣r♦r♠ t str♥②ss ♦ ♠① rs t♦ ♦♦♥ st♦♥s ♣rs♥t t t♦ ♠♦st r♥t♣♣r♦s t♦ str ♠① t t♦♥ ts t ♠①tr ♦ ♦t♦♥ ♠♦s♥ ts ①t♥s♦♥ ♣r ♦ t♦♥ ♥tr♦s t ♥r♥ ss♥ ♠①tr♠♦ ♦♥s♦♥ s ♥ ♥ t♦♥

r ♦ s♠♣ ♠t♦s t♦ str ♠①

t

♠t♦s

♥ ♦rr t♦ ♠♣s③ t ts ♥r♥t t♦ t ♠① t str♥ ♥♠rt tr ♥ t ♥♦t ♥t ♠t♦s ♣r♠t t♦ str s t♦r ♦ ts ♠t♦s ♠♥ r ♥ tr t② ♦ ♥♦trs♣t t ♥ ♦ r tr t② ♦ ♥♦t ♦♥sr r ♥ ts♥t s♣

❲♦ ♦♥t♥♦s ♠t♦ ♥ ♠② t♠♣t t♦ str t srt rss t② r ♦♥t♥♦s s ts ♠t♦ ♦♥ssts ♥ ♦♥rt♥ t t♦r♥ ♦r♥ ttrt s t♦ ♥♠r s ♠t♦ s♣ t♦ t ♦♥t♥♦st s t♥ ♣♣ t♦ ♣r♦r♠ t str ♥②ss s ♣♣r♦ ♠s r②str♦♥ ss♠♣t♦♥ ♦r t ♦r♥ rs ♥ t ss♠s tt tr s ts♠ ♣ t♥ t ♦♣s ♦ sss ♠♦ts ♦r♦r ts ♣♣r♦

Page 150: Model-based clustering for categorical and mixed data sets

r ♦ s♠♣ ♠t♦s t♦ str ♠① t

s t♦ rr ♥ t ♣rs♥ ♦ t♦r rs ♥ t ss♠s ♠♥♥ss ♦rr t♥ t ♠♦ts ♦r ♥st♥ t s ♥♦t ♣♦ss t♦ ♥♠r rs t♦ t♦r s ♦♦r ♦r ♦②

❲♦ srt ♠t♦ s ♠t♦ ♦♥ssts ♥ srt③t♦♥ ♦ t ♦♥t♥♦s rs s ♠t♦ s♣ t♦ t t♦r rs s s t♦ ♣r♦r♠t str ♥②ss ♦s ♦ t ♥♠rs ♦ ♠♦ts ♥ ♦ t ♦♥♦t♦♥s r t ♦r ts ♦s r r s♥ t② ♥♥ trsts ♦ t str ♥②ss ♦r♦r tr r t ♥♠rs ♦ t♦rs tsrt③t♦♥ ♣r♦ss s t♦ ♦ss ♦ ♥♦r♠t♦♥ ♥② t ♥trss ♣♥♥s r t② ♠♦③ s♥ t rs r ♦♥sr s t♦rs Prt

t♦r ♣♣r♦ t♣ t♦r ♥②ss ♠t♦ ♣r♠ts t♦ ♣r♦t♥s sr ② t♦r rs ♥ ♦♥t♥♦s s♣ s ②r♣♥ t srt rs ② tr t♦r ♦♦r♥ts ♥② ♠t♦ s♣ t♦t ♦♥t♥♦s rs ♥ s t♦ str t t st ♦r ♥♦t tt♥ ts s♣ s ♦♥t♥♦s ♥s ♥ t ♦♥② ♥t ♥♠r ♦ ss ♣♥♦♠♥♦♥ s♦ ♥rss t rs ♦ ♥r② ♦r♦r t rsts rss ♠♥♥ s♥ t rs r ♥♦t str ♥ tr ♥t s♣ ♥t ♥tr♣rtt♦♥ ♦ t sss s ♦♥ ② t ♣r♠trs ♦ t t♦r s♣

tr ♠t♦s ♣rs♥t ♦ r ♥♦t ♥t s♥ t② ♦ ♥♦t rs♣tt ♥tr ♦ r s ♥ t ♦♦♥ t st ♠t♦s ♠♦③t strt♦♥ ♦ t rs ♥ tr ♥t s♣

①t♥s♦♥ ♦ ss ♠t♦s ♦r ♠① t

♠♥s ♦rt♠ ♠♥s ♦rt♠ ♦♥② rqrs ♥t♦♥ ♦ st♥ t♥ t ♥s t♦ str ♥② ♥ ♦ t r♥t st♥ ♠srs ♥ st ♦r ♠① t s ♦r ♥st♥ t sst♦♥s ♦ ❩ ♥❬❪ ♥ ♦ ♠ ♥ ② ❬❪ ♦s② ts ♣♣r♦ ♣s trs ♦ t ♦♠tr ♠t♦s sss ♥ t♦♥

♦♥t♦♥ ♥♣♥♥ ♠①tr ♠♦ ♦ rr♥ strt♦♥♦r ♠① t s ♣r♦♠ t♦ ♣r♦r♠ t str ♥②ss t ♠①tr ♠♦ss ♣r♦♠ s s② ♦ ② t ♦♥t♦♥ ♥♣♥♥ ♠♦ s t♦♥ ♥ ♦♠♣♦♥♥t strt♦♥ s ♥ ② t ♣r♦t ♦ ♥rt strt♦♥s s ss strt♦♥s ♥ s s t ♦♥♠♥s♦♥♠r♥ strt♦♥s ♦ t ♦♠♣♦♥♥ts s ♣r♦♣♦s ② r ❬❪ ♥ ② ♦st ♥ P♣♦r♦ ❬P❪ ♦♥ssts ♥ stt♥ t ♦♥♠♥s♦♥ ♠r♥ strt♦♥s ② ss strt♦♥s s ♠♦r ♥♦t♦♥ ♦r t ♦♥t♦♥ ♥♣♥♥ ♠♦ s s ♥ ts ss♠♣t♦♥ s ♦t♦ ♣rs♥t t♦ ♠♥ ♣♣r♦s r① t ♦♥t♦♥ ♥♣♥♥ss♠♣t♦♥

Page 151: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss ♦ ♠① t sts stt ♦ t rt

①tr ♦ ♦t♦♥ ♠♦s ♥ ts ①t♥s♦♥

♣r ♦s

t ♦t♦♥ ♠①tr ♠♦ ♥tr♦ ② ❲ r③♥♦s ❬r③❪♦s t♦ str t sts t ♦♥t♥♦s ♥ t♦r rs

♥ t ♦♥t♥ts t ♦ t♦r rs ♥t♦ s♥ ♦♥ ♦♦s ♠t♥♦♠ strt♦♥ ♦r♦r t ss♠s tt t ♦♥t♥♦srs ♦♦ ♠trt ss♥ strt♦♥ ♦♥t♦♥② ♦♥ t ss ♥♦♥ ♠♦t② r♦ss♥ ♦r ♣rs② ts ♠♥s ♣♥ ♦♥ ♦t ss ♥t♦r rs s t ♦♥t♦♥ ♣♥♥② t♥ t ♦ rss t♥ ♥t♦ ♦♥t

♦t♦♥ ♠♦

♠ ♥ ♥ t ❬❪ ♥♦t tt t rs♥ r♦♠ ①♣r♠♥tt♦♥s ♥♣s②♦♦② ♦t♥ ♦♥t♥ ♦t srt ♥ ♦♥t♥♦s rs ♦ t② ♥tr♦t ♦t♦♥ ♠♦ t♦ ♠srs ♦ ss♦t♦♥ t♥ t rs ♦ st sts

♥ ♦t♦♥ ♠♦ ♥s t ♠t♥♦♠ strt♦♥ ♦♥ t ♦t♦r rs ♥ ♠trt ss♥ strt♦♥ ♦♥ t ♦♥t♥♦srs ♦♥t♦♥② ♦♥ t t♦r ♦♥s ♦r ♣rs② t st ♦ t t♦r rs x

i s ♦♥sr s ♦♥ t♦r rs ♦♦s r♠t♥♦♠ strt♦♥ Mm(λ1, ..., λm) s λh ♥♦ts t ♣r♦t② tt x

i

ts t ♠♦t② r♦ss♥ h ♦r♦r ♦♥t♦♥② ♦♥ x

i t♥ t ♠♦t②r♦ss♥ h t crt ♦♥t♥♦s r x

i ♦♦s crt ss♥ strt♦♥ Nc(µ

h,Σ) s t t♦r rs ♥♥ t ♠♥ ♦ t ♦♥t♥♦srs t ♥♦t tr s♣rs♦♥

♦tt♦♥s s t st ♦ t t♦r rs x

i s ♦♥sr ② t ♦t♦♥♠♦ s ♦♥ t♦r rs s ♦♠♣t s♥t ♦♥ s sxhi = 1 t ♥ ts t ♠♦t② r♦ss♥ h ♥ xhi = 0 ♦trs

♥t♦♥ ♦t♦♥ ♠♦ t♦r ♦ ♠① rs xi = (x

i ,x

i ) sr♥ ② ♦t♦♥ ♠♦ ts ♣ s rtt♥ s ♦♦s

p(xi;α) =m∏

h=1

(

λhφc(x

i ;µh,Σ)

)xhi ,

r α r♦♣s t s♣rs♦♥ ♠tr① Σ ♥ t t♦r (λh,µh;h = 1, . . . ,m)

①tr ♦ ♦t♦♥ ♠♦s

♦t♦♥ ♠♦ s ①t♥ t♦ t ♠①tr r♠♦r ♥ t ♠①tr♦ ♦t♦♥ ♠♦s s s ♥ sr♠♥♥t ♥②ss ② ❲ r③♥♦s ❬r③❪

Page 152: Model-based clustering for categorical and mixed data sets

①tr ♦ ♦t♦♥ ♠♦s ♥ ts ①t♥s♦♥ ♣r ♦s

♥ ♥ str ♥②ss ② r♥ ♥ ❲ r③♥♦s ❬❪ ♥ ♦rr t♦t ♥t♦ ♦♥t t ♥trss ♣♥♥s

♥t♦♥ ①tr ♦ ♦t♦♥ ♠♦s t♦r ♦ ♠① t xi =(x

i ,x

i ) s r♥ ② ♠①tr ♦ ♦t♦♥ ♠♦s t ♣ ♦ ts ♦♠♣♦♥♥t ks rtt♥ s ♦♦s ♦r k = 1, . . . , g

p(xi;αk) =m∏

h=1

(

λhkφc(x

i ;µhk,Σ)

)xhi ,

r αk r♦♣s t s♣rs♦♥ ♠tr① Σ ♥ t t♦r (λhk,µhk;h = 1, . . . ,m)

♥trss ♣♥♥s ♠①tr ♦ ♦t♦♥ ♠♦s ♦♥srs t ♥trss ♣♥♥s ♣r ♦♣ ♦ rs s ♦♦s

♦t rs r t♦r tr ♥trss ♣♥♥s r ♠♦③② t ♠t♥♦♠ strt♦♥s

♦t rs r ♦♥t♥♦s tr ♥trss ♣♥♥s r ♠♦③② t rt ss♥ strt♦♥s

♦♥ r s t♦r ♥ ♦♥ r s ♦♥t♥♦s tr ♥trss♣♥♥s r ♠♦③ ② t ♥♥ ♦ t t♦r r ♦♥ t♠♥s ♦ t ss♥ strt♦♥s ♦ t ♦♥t♥♦s r

♥tt② s ♣♦♥t♦t ② ❲s ♥ ♦ ❬❲❪ t ♠①tr ♦♦t♦♥ ♠♦s s ♥♦t ♥t s ♦ t ♥tr♠♥② ♦ ss ♠♠rs♣st ♦t♦♥ ♥ ♦rr t♦ ♦r♦♠ ts ♦ ♥tt② ts t♦rs s♦♠ ♦♥str♥ts ♦♥ t ♠♥ ♣r♠trs ♦ t ss♥ strt♦♥s

♥♠♥s♦♥ ♠r♥ strt♦♥s ❲ ♠♣s③ tt t ♦♥t♦♥ ♥♣♥♥ ♠♦ s ♠♥♥ s♥ ts ♦♥♠♥s♦♥ ♠r♥ strt♦♥s ♦ ts ♦♠♣♦♥♥ts r ss ♦r ♥st♥ t② ♦♥sst ♥ ♠t♥♦♠ ♦r ss♥ strt♦♥s ♦r t ♠①tr ♦ ♦t♦♥ ♠♦s t ♦♥♠♥s♦♥♠r♥ strt♦♥s ♦ t t♦r rs ♦r ♦♠♣♦♥♥t r ss s♥t② r ♠t♥♦♠ strt♦♥s ♦r t ♦♥♠♥s♦♥ ♠r♥ strt♦♥s ♦ t ♦♥t♥♦s rs ♦r ♦♠♣♦♥♥t r ♥♦t ss ♥ t②♦♥sst ♥ ♠①tr ♦ ♦♠♦sst ss♥ t m ♦♠♣♦♥♥ts

Pr♠tr st♠t♦♥ ♥r♥ ♥ s② ♣r♦r♠ ♥ ♦t rq♥tst♥ ②s♥ r♠♦rs ♥ t t♦rs ♦♥② ♣rs♥t t ♥ t rq♥tst ♦♥ ♠ ♥ ♦t♥ ② t ♦♦♥ ♠ ♦rt♠

Page 153: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss ♦ ♠① t sts stt ♦ t rt

trt♥ r♦♠ ♥ ♥t θ[0] trt♦♥ [r] s rtt♥ s st♣ t ♦♥t♦♥ ♣r♦ts

tik(θ[r]) =

π[r]k p(xi;α

[r]k )

p(xi;θ[r])

.

st♣ ♠①♠③t♦♥ ♦ t ①♣tt♦♥ ♦ t ♦♠♣tt ♦♦♦

π[r+1]k =

n[r]k

n, λ

h[r+1]k =

1

n[r]k

n∑

i=1

tik(θ[r])xhi ,

µh[r+1]k =

1

n[r]k

n∑

i=1

tik(θ[r])xhi x

i ,

Σ[r+1] =

1

n[r]

g∑

k=1

mj∑

h=1

n∑

i=1

tik(θ[r])xhi (x

i − µh[r+1]k )′(x

i − µh[r+1]k ),

r n[r]

k =∑n

i=1 tik(θ[r])

♦rt♠ ♠ ♦rt♠ ♦r t ♠①tr ♦ ♦t♦♥ ♠♦s

♠♣ ♥ s② ♦t♥ ② st♥ s ♣r♦r s ♣r♦r ss♠s♥♣♥♥ t♥ t ♣r♠trs ♥ sts ♦♥t strt♦♥s ♦r ♣r♠trs t s s♦ s② t♦ s s♠♣r s♥ t ♣r♠trs ①♣t ♣♦str♦r strt♦♥s

①tr ♦ ♦s ♦ ♦t♦♥ ♠♦

♥ ♥♠r ♦ ♣r♠trs rqr ② t ♠①tr ♦ ♦t♦♥ ♠♦s♥rss t t ♥♠r ♦ t♦r rs ♥ t t ♥♠r ♦ tr♠♦ts s ♦r♥s♥ ♥ ♥t ❬ ❪ ♣r♦♣♦s ♥ ①t♥s♦♥ ♦ts ♠♦ ♥ tr ①t♥s♦♥ t rs r s♣t ♥t♦ ♦♥t♦♥② ♥♣♥♥t♦s s tt ♦ s ♦♠♣♦s t t ♠♦st ♦♥ t♦r r♦r♦r ♦ ♦ rs ♦♦s ♦t♦♥ ♠♦

♥t♦♥ ①tr ♦ ♦s ♦ ♦t♦♥ ♠♦ t♦r xi ♦♠♣♦st ♦♥t♥♦s ♥ t♦r rs rss r♦♠ ♠①tr ♦ ♦s ♦ ♦t♦♥♠♦ t ♣ ♦ ts ♦♠♣♦♥♥t k s rtt♥ s ♦♦s ♦r k = 1, . . . , g

p(xi;αk) =∏

b=1

p(xbi ;αkb),

r αk = (αkb; b = 1, . . . ,) ♥ t ♣ ♦ ♦ b ♦r ♦♠♣♦♥♥t k s rtt♥

Page 154: Model-based clustering for categorical and mixed data sets

①tr ♦ ♦t♦♥ ♠♦s ♥ ts ①t♥s♦♥ ♣r ♦s

s ♦♦s ♦r b = 1, . . . ,

p(xi;αkb) =

φdkb(xbi ,µkb,Σkb) xb

i s ♦♥t♥♦smb∏

h=1

(λkh)xbhi xb

i s t♦r

mb∏

h=1

(

λhkbφdkb−1(xbi ;µh

kb,Σkb))x

bhi

xbi s ♠①

r x

bi ♥ x

bi r rs♣t② t ♦♥t♥♦s ♥ t t♦r rs

♦ ♦ b

t ♠♦s tr r ♦♥② ♦♥t♥♦s rs c = e ♥ d = 0 ♥ t

rs r t ♥t♦ t s♠ ♦ x1i = xi t♥ t ♠♦

s q♥t t♦ t tr♦sst ss♥ ♠①tr ♠♦ tr r ♦♥② ♦♥t♥♦s rs c = e ♥ d = 0 ♥

♦ s ♦♠♣♦s t ♦♥② ♦♥ r xbi = xbi ♦r b = 1, . . . , c

t♥ t ♠♦ s q♥t t♦ t ss♥ ♠①tr ♠♦ t ♦♥t♦♥♥♣♥♥ ss♠♣t♦♥ Σk s ♦♥

tr r ♦♥② t♦r rs c = 0 ♥ d = e t♥ t ♠♦s q♥t t♦ t t♥t ss ♠♦ s t♦♥

Pr♠tr st♠t♦♥ ♥r♥ s s② ♣r♦r♠ ♥ t ♦♥t♦♥ ♥♣♥♥ t♥ t ♦s ♦s t♦ ♣t t ♠ ♦rt♠ ♣rs♥t♥ ♦rt♠ ♥ ♦rr t♦ ♦t♥ t ♠ ♦ t ♠①tr ♦ ♦s ♦ ♦t♦♥♠♦ ②s♥ ♥r♥ ♦ ♣r♦r♠ ② s s♠♣r t ♣r♦rsr ss♠ t♦ ♥♣♥♥t ♥ ♦♦ ♦♥t strt♦♥s

♦ st♠t♦♥ ♦ t r♣rtt♦♥ ♦ t rs ♥t♦ ♦s t♦rs st♠t t r♣rtt♦♥ ♦ t rs ♥t♦ ♦s ② ♥ s♥♥ ♠t♦t ① ♥♠r ♦ sss ♥ ♥ ①st ♣♣r♦ s ♥♦t ♦ st♦♥ ♠ ♦ ts s♥♥ ♠t♦ s t♦ ♦♣t♠③ ♥ ♥♦r♠t♦♥ rtr♦♥ s ♠t♦ s ♥t③ ② t ♦② ♥♣♥♥t ♠♦ ♥ r♥t♠♦s r ♣r♦♣♦s ② s♥ t ♥trss ♣♥♥s ♦♠♣t t t rr♥t ♠♦

Pr♦r♠♥s ♦ t ♠①tr ♦ ♦s ♦ ♦t♦♥ ♠♦ s ♣rs♥t ♥❬❪ t ♠①tr ♦ ♦s ♦ ♦t♦♥ ♠♦ ♥ ♦t♣r♦r♠ t ♦② ♥♣♥♥t ♠♦ ♦r ts ♠♦ s t♦ ♠♥ rs rst ♦♥ s♦t t ss ♥tr♣rtt♦♥ s♥ t ♦♥♠♥s♦♥ ♠r♥ strt♦♥ ♦ ♦♠♣♦♥♥t s ♥♦t ss t r s ♦♥t♥♦s s♦♥ ♦♥ s ♦tt t② t♦ ♣r♦r♠ ♠♦ st♦♥ ♥ t ♣r♦♣♦s ♣♣r♦ ♥ s♦♣t♠ t♦ st t r♣rtt♦♥ ♦ t rs ♥t♦ ♦s rtr♠♦r t♦ ♦ t ♦rrt♦♥ ♦♥t t♥ ♦♥t♥♦s r ♥ t♦r

Page 155: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss ♦ ♠① t sts stt ♦ t rt

♦♥ s st ♦r ts ♦ s r s♥ t tr♠♥s t ♥tsr♥ t ♠♦ st♠t♦♥

❯♥r♥ ss♥ ♠①tr ♠♦

♥ ♥r♥ ss♥ ♠①tr ♠♦ ♥tr♦ ② rtt❬❪ ♣r♦r♠s t str ♥②ss ♦ t sts t ♦♥t♥♦s ♥ ♦r♥ rs ts ♠♥ ss♠♣t♦♥ s tt t ♦sr ♦r♥ ♥ ♥r② rs r♥rt r♦♠ ♥r②♥ ♥♦sr ♦♥t♥♦s rs ♦r♥ t♦ t s ♦ st ♦ trs♦s

♠r t♦r rs r ♥♦t ♦ t♦r rs①♣t t ♥r② ♦♥s ♥♥♦t ♠♦③ ② t ♥r♥ ss♥ ♠①tr♠♦ ♥ ts ♠♦ ss♠s ♥ ♦rr t♥ t ♠♦ts s ♥♦t♣rs♥t ♦r s rs

sr♣t♦♥ ♦ t ♥r♥ ss♥ ♠①tr ♠♦

ss♥ r ❲ ♦♥sr t t♦r yi = (x

i ,y

i ) r y

i s ♦♥t♥♦st♦r ♦ s③ d t♦r yi s ss♠ t♦ r♥ ② t ♦♠♦sst ss♥♠①tr ♠♦ ♦s t ♣ s

p(yi;θ) =

g∑

k=1

πkφe(yi;µk,Σ).

ss♥ t♥t r ♥ ♣rt t rs yi r ♥♦t ♦sr ♦rt② r rt t♦ t st ♦ t ♦sr srt rs x

i s ♦♦s

∀j = c+ 1, . . . , e, xjhi = 1 bjhk < yji ≤ bjh+1k ,

r bjhk < bjh+1k ♦r j = c + 1, . . . , e ♥ h = 1, . . . ,mj ♥ r bj1k = −∞ ♥

bjmj+1k = ∞ ♦♥s bjhk tr♠♥ t ♦sr srt rs r♦♠ tt♥t ♦♥t♥♦s ♦♥s s ♦t♥ tt t ♦sr rs xi = (x

i ,x

i ) t ♦♦♥ ♣

p(xi;θ) =

g∑

k=1

πk

Sk(x

i )

φe(yi,µk,Σ)dyi ,

r Sk(x

i ) s t ♦♠♥ ♦ t ♥trt♦♥ ♦ t♥t ss♥ rs yi rtt♦ t ♦sr srt rs x

i ♦r ♣rs② Sk(x

i ) = Sc+1k (xc+1

i )× . . . ×Sek(xei ) r t ♥tr Sjk(xji ) s ♥ ♦r j = c + 1, . . . , e s s Sjk(xji ) =

]bjhk , bjh+1k ] xjhi = 1

Page 156: Model-based clustering for categorical and mixed data sets

♦♥s♦♥

tr♥t ♦r♠ ♦ t ♣ ♥ tr♥t ♥ ♠♦r r♥② ♦r♠ ♦ t♣ ♥ ② s ♦t♥ ② ♥♦t♥ tt t ♦♥t♦♥ strt♦♥ ♦ x

i

♥ x

i s ♥ ♥r♥ ss♥ ♦♥ s strt♦♥ s t ♠♥ µ|k ♥

t ♦r♥ ♠tr① Σ| r ♥ ②

µ|k = µ

k +ΣΣ−1(x

i − µ

k) ♥ Σ| = Σ −ΣΣ

−1Σ,

r µ

k ♥ µ

k r rs♣t② t ♠♥s ♦ x

i ♥ ♦ x

i ♥ r t ♦

r♥ ♠tr① Σ =

[

Σ Σ

Σ Σ

]

s ♦♠♣♦s ♥t♦ s♠trs ♦r ♥st♥

Σ s t s♠tr① ♦ Σ ♦♠♣♦s ② t r♦s ♥ t ♦♠♥s rt t♦ t♦sr ♦♥t♥♦s rs s tr♥t ♦r♠ ♦ t ♣ ♦s s t♦ ♥t ♥r♥ ss♥ ♠①tr ♠♦

♥t♦♥ ❯♥r♥ ss♥ ♠①tr ♠♦ t xi t t♦r ♦ e ♠①rs r♥ t ♥r♥ ss♥ ♠①tr ♠♦ ts ♣ s rtt♥ s♦♦s

p(xi;θ) =

g∑

k=1

πkφc(x

i ;µ

k,Σ)

Sk(x

i )

φd(y

i ;µ|k ,Σ|)dyi .

r ♦♥t♦♥ ♦r ♠♦ ♥tt② ♥ t t♥t t♦r yi s ♥♦t♦sr tr s ♥♦ ♥♦r♠t♦♥ ♦♥ ts ♠♥ ♥ r♥ s t ♠♦ ss♠stt t ♠♥ts ♦ µ

k r ♥ ♥ tt t ♦♥ ♠♥ts ♦ Σ r q t♦♦♥

st♠t♦♥ ♦ t ♥r♥ ss♥ ♠①tr ♠♦

♥r♥ ② ♠①♠③t♦♥ ♦ t ♦♦♦ ♥t♦♥ s ♥♦t s② s♦ t ♣rs♥ ♦ d♠♥s♦♥ ♥trs ♥ ♥♦ ①♣t ♦r♠ ♥ Σ s ♥♦t♦♥

rtt ♣r♦♣♦ss t♦ ♣r♦r♠ t ♥r♥ ② s♥ s♠♣① ♠t♦ ♦ttt ts ♣♣r♦ ♠ts t ♦r t ♥♠r ♦ srt rs

♦♥s♦♥

❲ ♣♦♥t ♦t t ♠♣♦rt♥ t♦ ♦♥sr t rs ♥ tr ♥ts♣ ♦r t ♠① t r ♥♦t s② str ② ♠① ♠♦s s♦ t ♦ st♥r ♠trt strt♦♥ ♦r s rs s t ♠s t♦ ♣r♦♣♦s r♥t ♠trt strt♦♥ ♦r ♠① t ❲ ♣t tt ♦♥ t ♠♣♦rt♥ tt t ♦♥♠♥s♦♥ ♠r♥s ♦ ts strt♦♥s rss ♥ tt t ♣♥♥s r ♠♦③

♠♦ ♣rs♥t ♥ ♣tr ♦s t♦ ♣r♦r♠ t str ♥②ss ♦ tsts t ♦♥t♥♦s ♥ t♦r rs ② ♥ ts ♦ts ♦ttt ts stt♦♥ t st t ♦♥t♥♦s ♥ t♦r rs s t ♠♦stst ♦♥

Page 157: Model-based clustering for categorical and mixed data sets

♣tr str ♥②ss ♦ ♠① t sts stt ♦ t rt

♥ t ♦r♣② t t♦rs ♦ ♥♦t st② t s ♦ ♠① rs t♥tr s s t ♠♦ ♣rs♥t ♥ ♣tr s ♦ ♥trst ♥ t sr② ♥r s♥ t ♦s t♦ str t sts t ♥② ♥ ♦ rs ♠tt♥

Page 158: Model-based clustering for categorical and mixed data sets

♣tr

♦s str♥ ♦ ss♥

♥ ♦st strt♦♥s

s ♣tr ♥tr♦s s♣rs ♠①tr ♠♦ ♦r tsts t ♦♥t♥♦s ♥ t♦r rs ♦♠♣♦♥♥t strt♦♥s ♦ t ♦♥t♥♦s rs rss♥ ♦r♦r t t♦r rs r ss♠ t♦ ♥♣♥♥t ♦♥t♦♥② ♦♥ t ss ♥♦♥ t ♦♥t♥♦s rs ♥② ♦♥t♦♥② ♦♥t ♦♥t♥♦s rs t ♦♠♣♦♥♥t strt♦♥s ♦t t♦r rs r ♥r ♦st strt♦♥sr ♣r♠trs r ♥♦t ③r♦ ♥ t rrss♦♥ ♠①♠♠ ♦♦ ♥r♥ ♥ t ♠♦ st♦♥ r s♠t♥♦s② ♣r♦r♠ ② ♠ ♦rt♠♦r ① ♥♠r ♦ sss♠r ①♣r♠♥ts strt t ♠♦ r♥ ♥ str ♥②ss ♥ ♥ s♠s♣rs sst♦♥♥ ♥s r

str♠ t♦ts rà ♠♦t t♠♣s ♥♦ ♣♦t rr

r q t♦♠ t♦♠ t♦♠♥ ♦r sr

t strs ♦rt ♣r q ♦ str♦ ♦rt ♣r

r q t♦♠ t♦♠ t♦♠ ♥s ♣♦r♠ rrs st

♥tr♦t♦♥

❲ ♣rs♥t ♠①tr ♠♦ t♦ str t sts t ♦♥t♥♦s ♥ t♦rrs s ♠♦ s ♦ ♦t t♦ ♣r♦ ss ♦♥♠♥s♦♥♠r♥ strt♦♥s ♦r ♦♠♣♦♥♥t ♥ t♦ ♠♦③ t ♥trss ♣♥♥s

Page 159: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♥ ♦st strt♦♥s

♦r s ♠♦ t ♦♥t♥♦s rs ♦♦ ♠trt ss♥ strt♦♥ ♦r ♦♠♣♦♥♥t ♦♥t♦♥② ♦♥ t ss ♥ ♦♥ t ♦♥t♥♦srs t t♦r rs r ss♠ t♦ ♥♣♥♥t② r♥ ② ♥r♦st strt♦♥s rst♥ ♠♦ s s♦ ♥♠ ♠①tr ♠♦ ♦ ss♥♥ ♦st strt♦♥s

♥r ♦st rrss♦♥s r ss② s ② ♠①tr ♠♦s ♦r t♦r t ❬♦r❪ ♦r♦r t ♠t t♥t ss ♠♦ ❬❱r❪ ss t♥t♦♥t♥♦s rs t♦ ♠♦③ t ♥trss ♣♥♥s t♥ t t♦r rs ♦ t s ♥tr t♦ s ♠①tr ♠♦ ♦ ss♥ ♥ ♦ststrt♦♥s ♥ ♦t ♦ t ♦♥t♥♦s ♥ t♦r ♥s ♦ rs r♦sr

♣rs♠♦♥♦s rs♦♥ ♦ ts ♠♦ s ♥tr♦ ② ♥ s♦♠ ♦♥str♥ts ♦♥t ♦st ♣r♠tr s♣ ♦ t rst♥ ♠♦ s ♠♦r s② ♥tr♣rt ♥♥ ♣r♦r♠ ttr tr ♦ t♥ t s ♥ t r♥ ♦r ① ♥♠r♦ sss t st♦♥ ♦ t ♣rs♠♦♥♦s ♠♦ ♥ t ♣r♠tr st♠t♦♥r s♠t♥♦s② ♣r♦r♠ ② ♠ ♦rt♠ ♦♣t♠③s ♥ ♥♦r♠t♦♥rtr♦♥ r♥ ♦r ♥♠r ①♣r♠♥ts strt t r♥ ♦ t rtr♦♥ ♦♠♣r t♦ t rtr♦♥

♥② ♥ t ♠♦ s ♥tr♦ t♦ ♣r♦r♠ str ♥②ss t ♥ ♣♣ ♦r s♠s♣rs sst♦♥ ❬❩+❪ ♥ t s ♥♦♥ ttt ♥rt ♣♣r♦s ♥ ♦t♣r♦r♠ t ♠t♦s s♣ ♦ t sst♦♥♥ s ♣♥♦♠♥♦♥ s ♣rtr② ♦sr ♥ ♦srt♦♥s r ❬❪ ♥ ts ♠t♦s ①♣♦t t ♥♦r♠t♦♥s ♣rs♥t ♥ ♦t ♥ ♥ t sr♠♥t ♣♣r♦s t ♦♥② ♥t♦ ♦♥tt t ♥♦r♠t♦♥ ♥ t② r♥ sst♦♥ r ♦♥② ♦♥ t t

trtr ♦ ts ♣tr s rt s ♦r♥③ s ♦♦s t♦♥ ♣rs♥ts t ♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s ♦r t stst ♦♥t♥♦s ♥ t♦r rs s ♠♦ ♣r♦r♠s t str ♥②ss ② ♣r♦♥ ss ♦♥♠♥s♦♥ strt♦♥s ♦r ♦♠♣♦♥♥t ♥ ②♠♦③♥ t ♥trss ♣♥♥s ♥ str♥ r♠♦r t♦♥ s♦t t♦ t ♠①♠♠ ♦♦ st♠t♦♥ t♦♥ ♣rs♥ts t ♠ ♦rt♠ s♠t♥♦s② ♣r♦r♠s t st♠t♦♥ ♦ ♦t ♠♦ ♥ ♣r♠trs② ♦♣t♠③♥ ♥ ♥♦r♠t♦♥ rtr♦♥ t♦♥ ♣rs♥ts r♥t ♥♠r ①♣r♠♥ts ② strt t r♥ ♦ t rtr♦♥ t♦ ♣r♦r♠ t ♠♦st♦♥ ♦r♦r t② s♦ t ♣r♦r♠♥s ♦ t st♠t♦♥ ♦rt♠ ♥t ♠♦ r♦st♥ss t♦♥ ♣rs♥ts ♦♥ ♣♣t♦♥ ♥ str♥ ♥ ♦♥♣♣t♦♥ ♥ s♠s♣rs sst♦♥ ♦♥ t♦ r t sts ♦♥s♦♥ s♥ ♥ t♦♥

t t xi = (x1i , . . . , xci ,x

c+1i , . . . ,xei ) t ert t♦r ♦ ♠①

rs rst c rs r ♦♥t♥♦s ♥ ts sst ♦ rs s ♥♦t② x

i st d rs r t♦r rs s♥ s♥t ♦♥ ♥ts sst ♦ rs s ♥♦t ② x

i ♦t tt c+ d = e

Page 160: Model-based clustering for categorical and mixed data sets

①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s

①tr ♠♦ ♦ ss♥ ♥ ♦st str

t♦♥s

♠ ♠♦ ♣r♦r♠s t str ♥②ss ♦ ♦♥t♥♦s ♥ t♦r tst t ♦ ♦t t♦ ♣r♦ ss ♦♥♠♥s♦♥ ♠r♥ strt♦♥s ♦r ♦♠♣♦♥♥t ♥ t♦ ♠♦③ t ♥trss ♣♥♥s

♥ ♣ ♦ ♦♠♣♦♥♥t s ♥ ② t ♣r♦t t♥ t♣ ♦ t ♦ ♦♥t♥♦s rs ♥ t ♣ ♦ t ♦ t♦r rs♦♥t♦♥② ♦♥ t ♦ ♦♥t♥♦s rs ♦r ♣rs② ♦♥r♥♥ t♦♥t♥♦s rs t② ♦♦ ♠trt ss♥ strt♦♥ ♦r ♦♠♣♦♥♥t ♦♥r♥♥ t t♦r rs t ♠♦ ss♠s tr ♥♣♥♥♦♥t♦♥② ♦♥ ♦t t ss ♠♠rs♣ ♥ t ♦♥t♥♦s rs

♥t♦♥ ①tr ♠♦ rt t♦ ♥ t♦r xi s r♥ ② ♠①tr ♠♦ rs♣t♥ ♥ t ♣ ♦ ts ♦♠♣♦♥♥t k s rtt♥ s♦♦s ♦r k = 1, . . . , g

p(xi;αk) = p(x

i ;αk)p(x

i |x

i ;αk)

= φc(x

i ;µk,Σk)e∏

j=c+1

p(xji |x

i ;βkj),

r αk = (µk,Σk,βk) ♥♦ts t ♦ ♦♠♣♦♥♥t ♣r♠trs r t t♦rµk ∈ R

d ♥♦ts t ♠♥ ♦ t ♦♥t♥♦s rs ♦r ♦♠♣♦♥♥t k ♥ r t♠tr① Σk ♥♦ts tr ♦r♥ ♠tr① t♦r βk = (βkj; j = c+ 1, . . . , e)♥♦ts t ♦ ♣r♠trs ♦ ♦♠♣♦♥♥t k r rt t♦ t t♦rrs ♥ t t♦r βkj r♦♣s t ♣r♠trs rt t♦ t strt♦♥♦ t t♦r r x

ji ♦r ♦♠♣♦♥♥t k

♥ ♠♦ ss♠s tt ♦r ♦♠♣♦♥♥t t strt♦♥ ♦ t♦r r s ♥r ♦st rrss♦♥ ♦s t ①♣♥t♦r② rs r t ♦♥t♥♦s ♦♥s

♥t♦♥ ①tr ♠♦ ♦ ♦st rrss♦♥s ❲t t ♥♦tt♦♥ x0 = 1t ♦♠♣♦♥♥t strt♦♥s ♦ xji ♦r j = c+1, . . . , e r ♥ ② t ♦♦♥♣ ♦r k = 1, . . . , g

p(xji |x

i ;βkj) =

mj∏

h=1

exp(

∑cj′=0 β

j′hkj x

j′

i

)

∑mj

h′=1 exp(

∑cj′=0 β

j′h′

kj xj′

i

)

xjhi

,

r t ♣r♠trs βkj = (βj′hkj ; j

′ = 0, . . . , c;h = 1, . . . ,mj) ∈ Rc+1 ♥♦ts t

♦st ♣r♠trs ♦ t t♦r r xji ♦r ss k ♥ ♦rr t♦ ♥sr t

♠♦ ♥tt② ♣t ∀(k, j, j′), βj′1kj = 0 ♥② ♥♦t tt t ♣r♠tr

β0hkj s t ♥tr♣t ♦ t ♦st rrss♦♥ t ♦tr ♣r♠trs βj

′hkj ♦r

j′ = 1, . . . , c r t s♦♣ ♣r♠trs

Page 161: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♥ ♦st strt♦♥s

P♦t♥t r ♥♠r ♦ ♣r♠trs ♠♦ ♥ rqr r ♥♠r♦ ♣r♠trs ♥ t ♥♠r ♦ ♣r♠trs ♥♦ ② t ♠♦ s q t♦

(g − 1) + g

(

c(c+ 3)

2

)

+ ge∑

j=c+1

(mj − 1)(c+ 1).

♥ ♦rr t♦ ♦t♥ ttr sr♥ tr♦ ♥tr♦ ♣rs♠♦♥♦srs♦♥ ♦ t ♠♦ ② r♥ t s♣ ♦ t ♦st ♦♥ts

♣rs ♦st ♥t♦♥s ♦r t ♠① ♦♥t♦♥ ♣♥♥s s♣rst② ♦ t ♠♦ s ♥ ② t srt ♣r♠trs δkj = (δj

kj; j′ = 0, . . . , c)

♥ δj′

kj = 1 t♦r r j s ♦♥t♦♥② ♣♥♥t ♦♥ ♦♥t♥♦s

r j′ ♦r ♦♠♣♦♥♥t k ♥ δj′

kj = 0 ♦trs ♦t tt δ0kj = 0 ♥♦s ♥♥tr♣t ♥ t ♦st rrss♦♥ ♦ t♦r r j ♦r ♦♠♣♦♥♥t k sδkj ①s s♦♠ ♦st ♣r♠trs t♦ ③r♦ s♥ βkj ∈ S(δkj) t

S(δkj) =

βkj : ∀(k, j, j′) s s δj′kj = 1, t♥ ∀h βj′hkj = 0

.

♦♥tr♦ ♥♠r ♦ ♣r♠trs ♥♠r ♦ ♣r♠trs rqr ② t♥r ♠♦ ♥ ② (g, δ) s q t♦

(g − 1) + g

(

c(c+ 3)

2

)

+

g∑

k=1

e∑

j=c+1

c∑

j′=0

δj′

kj(mj − 1).

♥♥ ♠♦ ♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s ♣r♦s ♠♥♥ sss ♥ ss ♥ s♠♠r③ ② ♣r♠trs♠♥ ♥ r♥ ♦r t ♦♥t♥♦s rs ♥ ♣r♦t② ♦ ♠♦t② ♦r t♦r r q t♦

p(xjh = 1|zik = 1) =

Rc

φc(x

i ;µk,Σk)exp

(

∑cj′=0 β

j′hkj x

j′

i

)

∑mj

h′=1 exp(

∑cj′=0 β

j′h′

kj xj′

i

)dx

i .

t♦ ts ♥tr s ♥♦t ①♣t t s s② ♣♣r♦①♠t ② ♠♠ ♠t♦rtr♠♦r ♦r ss t ♣♥♥s t♥ t ♦♥t♥♦s rs r♠♦③ ② t ♦rrt♦♥ ♠tr① t t♦r r xji s ♦♥t♦♥②♣♥♥t t t ♦♥t♥♦s ♦♥ xj

i δj′

kj = 1

♣♥♥s ♥t♦r r s ♥ ①♠♣ ♦ t ♣♥♥s t♥ rs t♥ ♥t♦ ♦♥t ② t ♠♦ ♥ t♥ rs ♥♦ts ♣♥♥② t♥ rs ♥ ♥ s♥ ♦ ♥ ♥♦ts ♦♥t♦♥ ♥♣♥♥ ♦t tt t ♦sr rs r ♥ t zi tr s qt♥ t ♦♥t♥♦s rs t t♦r rs r ♥♦t ♥ t♦tr♥ t ♥trss ♣♥♥② t♥ t ♦♥t♥♦s ♥ t t♦r rsr ♥ ② t srt ♣r♠trs δ

Page 162: Model-based clustering for categorical and mixed data sets

①♠♠ ♦♦ st♠t♦♥ ♥ ♦rt♠

zi

x1i

x2i

x3i

x4i

r ①♠♣ ♦ t ♣♥♥s t♥ ♥t♦ ♦♥t ② t ♠♦ rx

i = (x1i , x2i ) ♥ x

i = (x3i ,x

4i ) t δ

1k3 = δ2k3 = δ1k4 = 1 ♥ δ2k4 = 0

♥r ♥tt② ♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥ss ♥r② ♥t ts ♦ t ♣r♦♦ r ♥ ♥ ♣♣♥① ♠♦♥strt♦♥ s s♣t ♥ t♦ ♣rts rst② s♠ ♦r t ♣♦ss s ♦x

i t♦ ♦t♥ ♠①tr ♦ ss♥ strt♦♥s ♥ t♦ s ts ♥tt② rsts❬ ❨❪ ♦♥② s♦ t ♥tt② ♦ t ♣r♠trs ♦ ♦st♥t♦♥

①♠♠ ♦♦ st♠t♦♥ ♥

♦rt♠

♥ ❲ ♦♥sr t s♠♣ ① = (x1, . . . ,xn) ♦♥ssts ♦ n ♥s ss♠ ♥♣♥♥t② r♥ ② ♠①tr ♠♦ ♦ ss♥ ♥ ♦ststrt♦♥s ♠ s s② ♦t♥ ② t ♦♦♥ ♠ ♦rt♠ s ♦rt♠ tt t ♦ s ♣r♦r♠ ♦r ① ♠♦ ♥ ② t ♦♣(g, δ)

♥r♥ ♦ t ♦st ♣r♠trs t t ♠ st♣ t ♠①♠③t♦♥s ♦♥ t♣r♦♣♦rt♦♥s ♥ ♦♥ t ss♥ ♣r♠trs r s② ♣r♦r♠ ♦r tst♠t♦♥ ♦ t ♣r♠trs rt t♦ t ♦st ♥t♦♥s ♥♦ t♦ s♦ ♥♦♥①♣t qt♦♥s ♦ t② r ss② ♦t♥ ② t♦♥♣s♦♥ ♠t♦♥ t ♠ s st t♦ st♠t t ♦st rrss♦♥ ♣r♠trs r ♥s r♥t ts

Page 163: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♥ ♦st strt♦♥s

trt♥ r♦♠ ♥ ♥t θ[0] trt♦♥ [r] s rtt♥ s st♣ t ♦♥t♦♥ ♣r♦ts

tik(θ[r]) =

π[r]k p(xi;α

[r]k )

p(xi;θ[r])

.

st♣ ♠①♠③t♦♥ ♦ t ①♣tt♦♥ ♦ t ♦♠♣tt ♦♦♦

π[r+1]k =

n[r]k

n, µ

[r+1]k =

1

n[r]k

n∑

i=1

tik(θ[r])xi,

Σ[r+1]k =

1

n[r]k

n∑

i=1

tik(θ[r])(xi − µ

[r+1]k )′(xi − µ

[r+1]k ),

β[r+1]kj = argmax

βkj∈S(δkj)

n∑

i=1

tik(θ[r]) ln p(xji |x

i ;βkj),

r n[r]k =

∑ni=1 tik(θ

[r])

♦rt♠ ♠ ♦rt♠

♦ st♦♥ ♦rt♠

♠ ♠♦ st♦♥ s ♦♠♥t♦r ♣r♦♠ s ♦ t st♠t♦♥ ♦t srt ♣r♠tr δ s ♥t t♦ ♦t♥ t ♣r♠tr δ ♠①♠③st ♥♦r♠t♦♥ rtr♦♥ ♦r ① ♥♠r ♦ sss

♥ t s ♥r② ♠♣♦ss t♦ ♥ t ♠ ♦r δ s♥ t ♥♠r ♦ ♦♠♣t♥ ♠♦s s 2g(c+1)d s t rtr♦♥ s ♣♥③t♦♥ ♦ t♦srt ♦♦♦ ♥ s ♥ ♠ ♦rt♠ ♠①♠③♥ t ♣♥③♦srt ♦♦♦ ❬r❪ s t♦♥ s t ♠ st♣ ♦♥ssts♥ ♠①♠③♥ t ①♣tt♦♥ ♦ t ♣♥③ ♦♠♣tt ♦♦

♦t♦♥ ♦ t ♠ st♣ t trt♦♥ [r] t ♠ st♣ ♦ t ♠ ♦rt♠♠s t tr♠♥♥ (δ

[r+1]kj ,β

[r+1]kj ) s s

(δ[r+1]kj ,β

[r+1]kj ) = argmax

δkj∈0,1c+1

argmaxβkj∈S(δkj)

n∑

i=1

tik(θ[r]) ln p(xji |x

i ;βkj)−νkj2

lnn,

r νkj =∑c

j′=0 δj′

kj ♥ts t ♥♠r ♦ ♣r♠trs rqr ② t ♦strrss♦♥ rt t♦ t♦r r j ♦r ♦♠♣♦♥♥t k

Page 164: Model-based clustering for categorical and mixed data sets

♠r ①♣r♠♥ts ♦♥ s♠t t sts

♠ ♦rt♠ t♦ ♦ t ♦♠♥t♦r ♣r♦♠s s♣ ♦ δs r s♦ ♥ ①st ♣♣r♦ t♦ tr♠♥ δ

[r+1]kj ♦r♥ t♦ s ♥♦t

♦ s ♣rr t♦ s ♠ rs♦♥ ♦ ts ♦rt♠ ♥ ts ♦rt♠t ①♣tt♦♥ ♦ t ♣♥③ ♦♠♣tt ♦♦ s st ♥rs t t ♠st♣ ♦ t trt♦♥ [r] ♦ t ♠ st♣ s s♥♥t ♥ s♥♥t ♠t♦s♥t③ ② (δ

[r]kj ,β

[r]kj)

♥ t ♠♣♦rt♥ ♦ sr ♥t③t♦♥s ♦t tt ts ♣♣r♦ ♣st ss ♣r♦♣rts ♦ t ♠ ♦rt♠ ♦ ts tr♠♥st ♦rt♠ ♦♥rs t♦ ♦ ♦♣t♠♠ ♦ t ♣♥③ ♦srt ♦♦ ♣♥s♦ t ♥t ♦ (δ[0],θ[0]) s t s ♠♥t♦r② t♦ ♣r♦r♠ ts ♦rt♠t r♥t ♥t③t♦♥s t♦ ssr t ♦♥r♥ t♦ ♦ ♠①♠♠ ♦ t♣♥③ ♦srt ♦♦

♠r ①♣r♠♥ts ♦♥ s♠t t sts

♠ ❲ ①♣r♠♥t② st② t ♦r ♦ t ♠ ♦rt♠ ♥ t ♣r♦r♠s t ♠♦ st♦♥ s♥ ♦t ss ♥♦r♠t♦♥ rtr ♥ rsts ttst t♦ t ♦♦ ♦r ♦ t ♠ ♦rt♠ ♦r t s♠t♥♦sst♠t♦♥ ♦ t ♠♦ ♥ t ♣r♠trs ♦r♦r t② s♦ tt t rtr♦♥ ♦t♣r♦r♠s t rtr♦♥ ♥ ts ttr ♦rst♠ts t ♥♠r♦ ♦♠♣♦♥♥ts ♥ t ♦♥t♦♥ ♣♥♥s t♥ ♠① t

trtr ♦ ts st♦♥ rst② t r s♠t ♦r♥ t♦ t ♠①tr♠♦ ♦ ss♥ ♥ ♦st strt♦♥s ♦♥② t r s♠t ♦r♥t♦ ♦tr ♠♦s

♠t♦♥s ② t s♣ ♠♦

♥♦♥ ♥♠r ♦ sss

t ♥rt♦♥ t r s♠♣ ♦r♥ t♦ t ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s t t♦ ♦♠♣♦♥♥ts ♥ q ♣r♦♣♦rt♦♥s ♥s rsr t t♦ ♦♥t♥♦s rs ♥ t♦ ♥r② ♦♥s ♠♦ ♣r♠trs r t ♦♦♥

µk = (ε(k − 2), ε(k − 1)), Σk =

[

1 k − 1.5k − 1.5 1

]

, δk1 = (1, 1, 0),

δk2 = (1, 0, 1), βj′2kj = ε,

r t ♣r♠tr ε ♦s t♦ ① t sss ♦r♣s rr s ε t ttrs♣rt r t sss ♦r t♦ sss ♦r♣s ♥ ♦r r♥t s③s ♦ s♠♣s(n = 50, 100, 200, 400) t sts r ♥rt

Page 165: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♥ ♦st strt♦♥s

st♠t♦♥ ♦♥t♦♥s ♦r t st tr ♠♦s r ♥ ♦♠♣tt♦♥ ttr ♠♦ t ♠♦ ♠①♠③♥ t rtr♦♥ ♥ t ♠♦ ♠①♠③♥t rtr♦♥ st♠t♦♥ ♦ t ♣r♠trs rt t♦ t tr ♠♦ s♣r♦r♠ ② t ♠ ♦rt♠ ♠①♠③♥ t ♦♦ ♦r ① ♠♦ ♦rt♠ t♦ ♦tr ♣r♠tr st♠t♦♥s r ♣r♦r♠ ② t ♠ ♦rt♠ ♠①♠③♥ ♥ ♥♦r♠t♦♥ rtr♦♥ ♥ s r♥♦♠ ♥t③t♦♥s♦ t st♠t♦♥ ♦rt♠ r ♦♥ ♦t tt ♥ ♥ ts s♠♣ s t s ♥♦t♦ t♦ ♣r♦r♠ ♥ ♠ ♦rt♠ ♦r ♠♦ s♥ tr r 212 ♠♦s ♥♦♠♣tt♦♥

sts ♥ ♣rs♥t t r r♥ ♦ t tr ♠♦m ♥ ♦ t st ♠♦ ♦r♥ t rs♣t② rtr♦♥ ♥♦t ②m rs♣t② m ❲ ♦♥ t♦ t ♦r ♦ t ♦rt♠ s♦r t ♣r♠tr st♠t♦♥ ♥ t r r♥ t♥s t♦ ③r♦♥ n r♦s ♦r t tr ♣♣r♦s rtr♠♦r ♦r ♥t s♠♣ s③ t♥♦r♠t♦♥ rtr ♦ t♦ r t r r♥ ② st♥ ss♦♠♣① ♠♦s

r♣ 10% 20%n m

m m

♥s ♦ t r r♥s ♥ s♣ ♠♦stt♦♥ r t ♥♠r ♦ sss s ♥♦♥

♠r ♦ sss ♥♥♦♥

t ♥rt♦♥ t r s♠♣ ♦r♥ t♦ ♠♦ sr ♥ t ♣r♦s s♠t♦♥ ♦r tr ♦r♣♣ sss ♥ ♦r r♥t s♠♣ s③s (n =50, 100, 200, 400) t sts r ♥rt

st♠t♦♥ ♦♥t♦♥s ♦r t st t st ♠♦s ♦r♥ t♦ t ♥ t rtr r st♠t ♦r g = 1, . . . , 4 ♥ s r♥♦♠ ♥t③t♦♥s ♦ t ♦rt♠ r ♦♥

sts s♣②s t ♠♥ ♦ t st ♥♠r ♦ sss ♥ t str♥ ♥① ❬❪ ♦♠♣t t t st♠t ♣rtt♦♥ ♦ t st ♥♠r ♦sss ♦r ♦t ♥♦r♠t♦♥ rtr ♦r ♦ t rtr♦♥ s ttrs♥ t ♥rst♠ts t ♥♠r ♦ sss ♥ t s♠♣ s③ s s♠ ♥♥ sss ♦r♣ rtr♠♦r ts ♦♥r♥ t♦ t tr ♥♠r ♦ ssss str t♥ ♦r t rtr♦♥ ♥ ts ttr ♦rst♠ts t ♥♠r ♦sss ♥ t t st s r st ♥ ♥① rt t♦ t ♠♦ ♦♣t♠③ t rtr♦♥ s s♠ s♥ ts ♥① s q t♦ ③r♦ ♥

Page 166: Model-based clustering for categorical and mixed data sets

♠r ①♣r♠♥ts ♦♥ s♠t t sts

g = 1 ♦t tt ts ♥① ts s ♦r t rtr♦♥ ♥ ts ttr♦rst♠ts t ♥♠r ♦ sss s ♥ ♠ tt ♥ t rtr♦♥♦rst♠ts t ♥♠r ♦ sss t s♣ts tr ss ♥t♦ t♦ sss

r♣ 05% 10% 20%n

♥s ♦ t st ♥♠r ♦ sss ♥ ♣♥ ♥ st ♥♥s ♥ ♣r♥tss ♦♠♣t ♦r ♦t ♥♦r♠t♦♥ rtr

♦♥t♦♥ ♥♣♥♥ stt♦♥

t ♥rt♦♥ t r s♠♣ ♦r♥ t♦ t ♦♠♣♦♥♥ts ♠♦ rt t♦r rs r ♦♥t♦♥② ♥♣♥♥t t♦ t ♦♥t♥♦s ♦♥s ts♣r♠trs r

µk = (k − 2, k − 1), Σk =

[

1 k − 1.5k − 1.5 1

]

, δk1 = (1, 0, 0),

δk2 = (1, 0, 0), β02kj = (−1)k/2.

st♠t♦♥ ♦♥t♦♥s ♦r t st t st ♠♦s ♦r♥ t♦ t ♥ t rtr r st♠t ② ♠ ♦rt♠ r♥♦♠② ♥t③ t♠st g = 2

sts s♣②s t ♦♥t r t ♦st ♥tr♣ts r ♥♦t♥ ♥ t ♦♥t r t ♦♥t♦♥ ♣♥♥② rt♦♥s♣ t♥ t♦r r ♥ ♦♥t♥♦s ♦♥ s ③r♦

n

δ0kj = 1 δ(1,2)kj = 1 δ0kj = 1 δ

(1,2)kj = 1

♦♥t r t ♦st ♥tr♣ts r ♥♦t ♥ (δ0kj = 1) ♥ ♦♥t r t ♦♥t♦♥ ♣♥♥② rt♦♥s♣ t♥ t♦r r♥ ♦♥t♥♦s ♦♥ s st♠t (δ

(1,2)kj = 1) ♦r ♦t ♥♦r♠t♦♥ rtr

❲ ♥♦t tt t rtr♦♥ s ttr ♦r s♥ t rtr♦♥♦rst♠ts s♦♠ rt♦♥s♣s t♥ rs ♥ t ♦♥t♦♥ ss♠♣t♦♥s

Page 167: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♥ ♦st strt♦♥s

♠t♦♥s ② ♠ss♣ ♠♦s

t ♥rt♦♥ t sts ♦ s③ r s♠♣ ♦r♥ t♦ t ♦♦♥♦♠♣♦♥♥ts ♦rrt ss♥ ♠①tr ♠♦

πk = 0.5, µ1j = 0.5, µ2j = −µ1j, Σ1 = Σ2 =

1 ε 0.5 εε 1 ε 0.50.5 ε 1 εε 0.5 ε 1

,

r ε s ♥ st♠♥t ♣r♠tr ε = 0 ♠♥② rs r ♦♥t♦♥②♥♣♥♥t ♥ ε s t rs r ♦♥t♦♥② ♣♥♥t t♦ st rs r srt③ t♦ ♦t♥ t♦r t t ♦r s]−∞,−1] ]− 1, 0] ]0, 1] ♥ ]1,∞[

st♠t♦♥ ♦♥t♦♥s st ♦♠♣♦♥♥t ♠♦ ♦r♥ t♦ ♥♦r♠t♦♥ rtr♦♥ s st♠t ② t ♠ ♦rt♠ ♥t③ t♠s

sts s♣②s t ♦♥t ♦ t ♥♦♥ ♦♥ts ♥ t ♦strrss♦♥ ♦r t ♦rrt rs ♥ t ε♦rrt rs ♦r♥t♦ ♦t rtr ♦r r♥t s ♦ ε ❲♥ ε = 0 t ♦♥t ♦ t ♦st rrss♦♥ t♥ t ε♦rrt rs t♦ s t

rtr♦♥ ♦rst♠ts t ♦♥t♦♥ ♣♥♥s ♦r t tts ttr t♣♥♥s t♥ t ♦tr rs

d

♦rrt ε♦rrt ♦rrt ε♦rrt

♦♥t r t ♦♥t♦♥ ♣♥♥s r ♠♦ ② ♦t♥♦r♠t♦♥ rtr

s ♦ t s ♦ t rtr♦♥ s♦♥ r♥ t ♥♠r ①♣r♠♥ts ♦r s t♦ s t rtr♦♥ t♦ ♣r♦r♠ t ♠♦ st♦♥ ♥ ts ttr ♥ ♥t s♦♠ ♦♥t♦♥ ♣♥♥s t♥ ♠① rs

♥②ss ♦ t♦ r t sts

ss t str♥

t st sr♣t♦♥ ♥ rt ss t ❬t❪ sr ♣t♥ts ♣r rs ♦♥t♥♦s t t♦r ♥ ♦♥ ♣rt ttrt ♥ s ♥ t ❯ ♠♥ r♥♥ r♣♦st♦r② ♣rtttrt s ♥r② r ♥t♥ t ♣rs♥ ♦ rt ss ❲ ♥ ts

Page 168: Model-based clustering for categorical and mixed data sets

♥②ss ♦ t♦ r t sts

♥♦r♠t♦♥ r♥ ♦r str♥ rtr♠♦r t s① ♥s ♥ ♠ss♥s r ♦♠tt

♦♠♦♥♦s ♠♦s str♥ ♦t tt ♦t ♥s ♦ rs r♠♣♦rt♥t ♦r t str ♥②ss ♥ t ♥②ss ♣r♦r♠ ♦♥ t ♦♥t♥♦srs ② ♠①tr ♦ ss♥ strt♦♥s sts ♦r sss t ♥②ss♣r♦r♠ ♦♥ t t♦r ♦♥s ② t t♥t ss ♠♦ sts tr sssr s♣②s t st♠t ♣rtt♦♥ ② t ss♥ ♠①tr ♠♦ ♥ trst ♦♠♣♦♥♥t ♠♣ r ♥ ② t ♠t♥♦♠ ♠①tr ♠♦ ♥ trst ♦rrs♣♦♥♥ ♠♣ r s s tt sss ♦r♣ ♥ trst t♦r ♠♣s ♥ ♦ ♥♦t ♥ t♦r ♠♣ r t st♠t sssr s♣rt

−3 −2 −1 0 1 2 3

−3

−2

−1

01

23

4

First principal component analysis

Sec

ond

prin

cipa

l com

pone

nt a

naly

sis

Class 1 Class 2Class 3Class 4

−3 −2 −1 0 1 2 3

−3

−2

−1

01

23

4

First principal correspondence analysis

Sec

ond

prin

cipa

l cor

resp

onde

nce

anal

ysis

Class 1 Class 2Class 3

r Prtt♦♥s st♠t ② t ♦♠♦♥♦s ♠♦ ♣rtt♦♥ ♦ tss♥ ♠①tr ♠♦ r♥ ♥ t rst ♦♠♣♦♥♥t ♠♣ ♣rtt♦♥ ♦ tt♥t ss ♠♦ r♥ ♥ t rst ♦rrs♣♦♥♥ ♠♣

s s♦♥ ② t ♦♥s♦♥ ♠tr① ♣rs♥t ♥ t ♣rtt♦♥s ♦t♥ ② t ♠♦ ♦r ♦♠♦♥♦s rs r r② r♥t r♦♠ t ♣rtttrt ♥ t ♦ t st r♥ ♥① ♦♠♣t t♥ t ♣rtt♦♥ ♦ t ss♥ ♠①tr ♠♦ ♥ t ♣rt ttrt s q t♦ t s q t♦ ♥ t s ♦♠♣t t♥ t ♠t♥♦♠ ♠①tr ♠♦ ♥t ♣rt ttrt st g = 2 t rr♦r rts r ♦r t ♦♥t♥♦ss ♥ ♦r t t♦r s ♥② ♦t ♣rtt♦♥s ♦ t ♦♠♦♥♦s♠♦s r r♥t s♥ tr st r♥ ♥① s r q t♦

tr♦♥♦s ♠♦s str♥ ❲ ♣r♦r♠ t str ♥②ss ♦♥t ♦ t st ② s♥ t♦ ♠♦s t ♦♥t♦♥ ♥♣♥♥ ♠♦ ♥t ♠①tr ♦ ss♥ ♥ ♦st strt♦♥s rsts s♣② ♥ ♠ tt t ♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s ttr

Page 169: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♥ ♦st strt♦♥s

rt ♦♥t♥♦s rs t♦r rsss ss ss ss ss ss ss ss s♥ ♣rs♥

♦♥s♦♥ ts t♥ t ♣rt ttrt ♥ t t♦ ♣rtt♦♥sst♠t ② t ♦♠♦♥♦s ♠①tr ♠♦s

♣♣r♦s t t strt♦♥ ♥ ts ♠♦ sts t♦ sss s♥ ts rtr♦♥ s r t ♦♥ ♦♠♣♦♥♥t ♥ t tr ♦♠♣♦♥♥ts ♦t tt t ♦♥t♦♥ ♥♣♥♥ ♠♦ ♦rst♠ts t ♥♠r ♦sss s♥ t rtr♦♥ sts tr sss t ♦ ② ♦♥sr♥ t ♦ t st t ♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s♦t♥s ♠♦r ♠♥♥ ♠♦ s♥ t s ss sss ♥ ss ♣r♠trs

♦♥ ♥♣ ♣r♦♣♦s ♠♦ rtr♦♥

♦♦♦ Pr♠trs

❱s ♦ t rtr♦♥ ♥ ♦ t ♦♦♦ ♥t♦♥ ♥ ♥♠r♦ ♣r♠trs ♦r ♦t ♦♠♣♦♥♥t ♠♦s ♥ ♦♠♣tt♦♥

st ♠♦ ♥tr♣rtt♦♥ ♠♦rt② ss 70% r♦♣s ♥s t♥t s♠st s ♦r t ♦♥t♥♦s rs ①♣t ♦r t r t ♦r♦r tr r♥s r s♠r t♥ t♠ ♦ t ♠♥♦rt② ss 30% t♦rrt♦♥ t♥ t ♦♥t♥♦s rs r str♦♥r s s♣② ♥ r ss ♦♥t♥♦s rs ♥♥ t t♦r ♦♥s ♥ ss r t♥♥ ss r r t♥ ♠♣ts t ♠♦st t♦r rs ♥ ss t s t r ♦♣s ♠♣t t ♠♦st t♦rr ♥ ss

r s♣②s t ♣rtt♦♥ ♦t♥ ② t ♦♠♣♦♥♥t ♠①tr ♠♦♦ ss♥ ♥ ♦st strt♦♥s ♥ t rst ♣♥ ♦ t P♠①t ❬❪❲ ♥ s tt t s♦♥ ①s s sr♠♥t t♥ ♦t st♠t sss

♦♠♣rs♦♥ t ♦tr ♣♣r♦s rr♦r rt ♦t♥ ② t ♠①tr♠♦ ♦ ss♥ ♥ ♦st strt♦♥s s 38% ♦r♥ t♦ ❬❪ t ♦♥t♦♥ ♥♣♥♥ ♠♦ ♥ t♠① ♦t♥ ♥ rr♦r rt ♦ 23% ♥②t trt♦♥ rr str♥ ♠t♦s ♠ssst♦♥ rt r♥♥t♥ 22% ♥ 46% ♦t tt t ♠♥s ♣♣r♦ ❬❪ ♦t♥s ♥ rr♦rrt ♦ 15% t t st♦♥ ♦ t ♥♠r ♦ sss s ♠♦r t

Page 170: Model-based clustering for categorical and mixed data sets

♥②ss ♦ t♦ r t sts

age

trestbps

chol

thalach

oldpeach

sex

cp

fbs

restcy

exang

slope

ca

tha

ss

age

trestbps

chol

thalach

oldpeach

sex

cp

fbs

restcy

exang

slope

ca

tha

ss

r ♣♥♥s t♥ t ♦♥t♥♦s rs tr♥s ♦♥ t t♥ t t♦r rs r ♦♥ t rt ♣r ss ♥ ♥♦s ♣♥♥② s♦ ♥♦ ♥ ♦♥t ♥ t ♦st rrss♦♥ ♥ t ss

−2 −1 0 1 2

−1

01

2

First principal component analysis mixte

Sec

ond

prin

cipa

l com

pone

nt a

naly

sis

mix

te

Class 1 Class 2

r Prtt♦♥ r♥ ♥ t rst ♦♠♣♦♥♥t ♠♣ ♦ t P♠①t

♥♦♠ s♠s♣rs sst♦♥

t st sr♣t♦♥ ♥♦♠ s ♥r ♦ s♥ t st srs ♣t♥ts ❬❪ ② ♦r ♦♥t♥♦s rs ♦♠♣♥ t♠ ♥ ②s ♥②rs ②r ♦ ♦♣rt♦♥ ♥ t t♠♦r t♥ss ② t♦ ♥r② rs s① ♥♣rs♥s♥ ♦ r ♥ ② ♦♥ stts r tt ♦t♦♠③ r♦♠ ♠♥♦♠ ♦r ♥♦t

sr♠♥t ♥ ♥rt ♣♣r♦s ♦♠♣rs♦♥ ❲ strt ttt ♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s ♥ ♦t♣r♦r♠ ss

Page 171: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♥ ♦st strt♦♥s

♠t♦s ♦ s♠s♣rs sst♦♥ s♣② ♥ ♦srt♦♥s r ♦ r♥♦♠② ♥ ♣rt ♦ t s ❲ ♦♠♣r t rr♦r rt ♦ t♥ ♣rtt♦♥ ♦t♥ ② ♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥st♦ t rr♦r rt ♦t♥ t ♦st rrss♦♥ ♣rs♥ts t ♠♥♦ t ♠sss②♥ rt ♦♠♣t ♦♥ r♥♦♠② ♥ ♣rtt♦♥ ♦r r♥t♣r♥ts ♦ ♠ss♥ s

% ♠ss♥ s

♣r♦♣♦s ♠♦ ♦st

♥ ♦ t ♠sss②♥ rt ♦♠♣t ♦♥ t ♥s ♥ ♠ss♥ ♠♠rs♣

♦ r♥t ♣♣r♦s ♦r t♦ r♥t ♦ts ❲ r♠♥ tt t♦st rrss♦♥ ♠ s t♦ rt② ♠♦③ t ♦rr t♥ t sss ♥ts ♠t♦s s ♦♣ s♣② ♦r t s♠s♣rs sst♦♥ ♠ ♦ t ♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s ♠♦s s ♠♦r♠t♦s ♥ t ♠♦③s t ♦ strt♦♥ ♦ t t

♦♠♠♥ts ♣rs♥t rsts r s ①♣t ❲♥ ♠♦rt② ♦ t ♥s s t ♦st rrss♦♥ ♦t♥s ♦r ♠sss②♥ rt ♦r ♥ ♠♦rt② ♦ t ♥s s ♥ t ♠♥ ♥♦r♠t♦♥ s ♦♥t♥ ② ts ♥s s r♥ ts ①♣r♠♥t ♦sr tt t♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s ♦t♣r♦r♠s t ♦st ♠♦♦r ♥ rt ♦ ♠ss♥ s rtr♠♦r ts rt r♠t② r♦s ♦rt ♦st rrss♦♥ ♥ r② ♥s r ts st②s st♦r t ♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s ♥ s 27.8% ♦t ♥s s r♦♠ ♠♥♦♠ t ♦st rrss♦♥ s ♦s t♦ t ♦rsrr♦r rt ♥ 95% ♦ t s r ♠ss♥

♥♥ sss ♥ str ♥②ss ❲ ♥♦ ♥tr♣rt ♦t sss ♦t♥♥ t ♥s r ♥ ♠♦rt② ss 60% r♦♣s ♥s♥ ♦♥ ♦♠♣♥② t♠ ♥ r♥t② trt ss s ♠♥② ♦♠♣♦s② ②♦♥ ♦♠♥ ♥ s♠ t♠♦r ♥ ts ss t r s t t♠♦r trr s t r rs ♥ t ♠♥♦rt② ss 40% ♥ t ♣t♥ts rt ♦♠♣♥② t♠ s s♦rtr ♦r ♦♠♣♥♠♥t st♦♣♣ ♥ r ttrt♠♥t s ♦ s ♣t♥ts r ♦r ♥r② t t♠♦r ♠♥② ♣rs♥t♦r t ♠♥ ♥ ♥rs♥ t r rs ss ♥tr♣rtt♦♥ s s ♦♥ t♠r♥ ♣r♠trs ♣rs♥t ♥ r

♦♥s♦♥ ♠tr① s♣② ♥ s♦s tt t st♠t ♣rtt♦♥s ♦s t♦ t sr stts ♠♦rt② ss ♥♦s s♠ rs ♦ t r♦♠♥♦♠ ts rs s r ♥ t ♠♥♦rt② ss

Page 172: Model-based clustering for categorical and mixed data sets

♦♥s♦♥

−2000 0 2000 4000 60000.00

000

0.00

020

density of time

C 1 C 2

0 50 100

0.00

00.

010

0.02

0

density of age

C 1 C 2

1960 1965 1970 1975 1980

0.00

0.05

0.10

0.15

density of year

C 1 C 2

−10 −5 0 5 10 15 20

0.00

0.10

0.20

density of thickness

C 1 C 2

female male

0.0

0.4

0.8

barplot of sex

C 1 C 2 C 1 C 2absence presence

0.0

0.4

0.8

barplot of ulcer

C 1 C 2 C 1 C 2

r P♦tt♥ ♦ t ♠r♥ ♣r♠trs ♦ t ♦♠♣♦♥♥t ♠①tr ♠♦♦ ss♥ ♥ ♦st strt♦♥s st♠t ♦♥ t ♥♦♠ t sts

♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s♠♦rt② ss ♠♥♦rt② ss

♥♦t ♦r♠ ♠♥♦♠ ♦r♠ ♠♥♦♠

♦♥s♦♥ ♠tr① t♥ t ♣rtt♦♥ st♠t ② t ♠①tr ♠♦♦ ss♥ ♥ ♦st strt♦♥s ♥ t sr stts

♦♥s♦♥

♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s s ♥ ♥t ♣♣r♦t♦ str t sts t ♦♥t♥♦s ♥ t♦r rs ♦ t s ♦♦ ♥r t♦ t ♥♦♥ ♠♦s ♥ ②s ♥ ♠①tr ♦ ♦t♦♥ ♠♦s ♥ts rs ts rst ♥t s t♦ t ♥t♦ ♦♥t t ♥trss ♣♥♥st♥ t rs s t ♣r♦♣♦s ♠♦ ♦s t ss ♥♦ ②t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥ ts s♦♥ ♥t s t♦ ♣ ssstrt♦♥s ♦r t ♦♥♠♥s♦♥ ♠r♥s ♦ ♦♠♣♦♥♥t ♥ t ♣rtt♦♥r s② s♠♠r③s ss ② t ♣r♠trs ♦ t ss strt♦♥s♥ ② t ♦st ♥t♦♥s

♣rs♠♦♥♦s rs♦♥s ♦ ♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s ♦ t♦ ♠♦ t ♠♥ ♦♥t♦♥ ♣♥♥s t♥ ♠① rss t ss ♥tr♣rtt♦♥ s sr ♠♦ st♦♥ ♥ t ♣r♠tr

Page 173: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♥ ♦st strt♦♥s

st♠t♦♥ r s♠t♥♦s② ♣r♦r♠ ♠ ♦rt♠ ♠①♠③♥ ♥ ♥♦r♠t♦♥ rtr♦♥ ♦r♥ t♦ ♦r ①♣r♠♥ts ♦r s t♦ s t

rtr♦♥ s t ♥♦r♠t♦♥ rtr♦♥ ♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s ♥ s ♥ s♠

s♣rs sst♦♥ ♦r♥ t♦ ♦r ♣♣t♦♥ t ♥ ♦♦ ♥r t♦t ss sr♠♥t♦♥ ♣♣r♦s s♣② ♥ ♥s r

Page 174: Model-based clustering for categorical and mixed data sets

♣tr

♦s str♥ ♦ ss♥

♦♣s ♦r ♠① t

♠①tr ♠♦ ♦ ss♥ ♦♣s s ♣rs♥t t♦str ♠① t r ♥② ♥s ♦ rs r♦ t② ♠t ♠t strt♦♥ ♥t♦♥ s ♣♣r♦ ♦s t♦ strt♦rr② ♥s♠♣ ♠trt ♥trss ♣♥♥② ♠♦s ♣rsr♥ ♥② ♦♥♠♥s♦♥ ♠r♥ strt♦♥s ♦ ♦♠♣♦♥♥t ♦ ♥trst ♦r t sttst♥ ②♣②♥ ts ♦r t ♠r♥ strt♦♥s ♦ ♦♠♣♦♥♥t r ss ♣r♠tr ♦♥s ♥ ♦rr t♦ ttt ♠♦ ♥tr♣rtt♦♥ ♥ t♦♥ t ♥trss ♣♥♥s r t♥ ♥t♦ ♦♥t ② t ss♥ ♦♣s ♣r♦ ♦♥ ♦rrt♦♥ ♦♥t ♥ ♦♦♣r♦♣rts ♣r ♦♣ ♦ rs ♥ ♣r sss ♠♦ ♥r③s r♥t ①st♥ ♠♦s ♥♦r ♦♠♦♥♦s ♥ ♠① rs ♥r♥ s♣r♦r♠ tr♦♣♦st♥s s♠♣r ♥ ②s♥ r♠♦r ♠r ①♣r♠♥ts strtt ♠♦ ①t② ♥ ts r♥

s ❩♦rq♥ t ♠ts ♥ ♦♣ s♦

t q t rss♠st♦s s r②♦♥s sr ♥ s ♣♦♥t

♣♦♥tà ♣r♥ ♥tôt P♦rq♦

Pr q ♦r s♦♥ sst ♣s é♣r♣é

sst rss♠ésr ♥ s ♣♦♥t

♠ê♠ s♣rt ♦♠♠♥ t s ♠rs

♥ ♦♥♥tr♥t s♦♥ s♣rtsr ♥ s t ♠ê♠ ♦s

í♦s ③♥t③ás①s ❩♦r

Page 175: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t

♥tr♦t♦♥

♠ ♦ ts ♣tr s t♦ ♣rs♥t ♠♦s str♥ ♦r ♠① t♦ ♥② ♥s ♦ rs ♠tt♥ ♠t strt♦♥ ♥t♦♥ s ♠♦s ♦ ♦t t♦ ♣rsr ss strt♦♥s ♦r ts ♦♥♠♥s♦♥♠r♥ strt♦♥s ♦ ♦♠♣♦♥♥t ♥ t♦ ♣rs♠♦♥♦s② ♥ ♠♥♥②♠♦③ t ♥trss ♣♥♥s

s ♦t ♥ ♥tr② ② t s ♦ ♦♣s ❬♦ ❪ ♥ ♦♣s ♠trt ♠♦ ② stt♥ ♦♥ t ♦♥ ♥ t♦♥♠♥s♦♥ ♠r♥s ♥ ♦♥ t ♦tr ♥ t ♣♥♥② ♠♦ t♥rs ♦r ♣rs② t t strt♦♥ s ♣♣r♦ ② ♣r♠tr♠①tr ♠♦ ♦ ss♥ ♦♣s ♦s t ♠r♥ strt♦♥s ♦ ♦♠♣♦♥♥t r ss ♥ ♦s t ss♥ ♦♣s ❬♦ ❲❪ ♠♦③ t♥trss ♣♥♥s ♦t tt ❬ ❪ r② s ♦♥ ss♥♦♣ t♦ ♥ strt♦♥ ♦ ♠① rs ♣r♦♣♦s ♠♦ s s♦ ♥r③t♦♥ ♦ ts ♣♣r♦ t♦ t ♥t ♠①tr ♠♦ r♠♦r

♥ ♠①tr ♠♦ s ♠♥♥ s♥ t ♣r♠ts tr s♠ ♦s r♥② ♥tr♣rtt♦♥ t ♣r♦♣♦rt♦♥s ♥t t ss ts t ♦♥♠♥s♦♥ ♠r♥ ♣r♠trs ♦ ♦♠♣♦♥♥ts r♦② sr t sss t ♦rrt♦♥ ♠trs r♥ ts sr♣t♦♥ ♥② ② s♥ t ♦♥t♥♦s t♥t strtr ♦ t ss♥ ♦♣s Pt②♣ s③t♦♥ ♣r ss♦s t♦ s♠♠r③ t ♠♥ ♥trss ♣♥♥s ♥ ♣r♦s sttr♣♦t ♦t ♥s ♦r♥ t♦ t ss ♣r♠trs

♦t tt ♦s♠s ♥ rs ❬❪ r♥t② s♠tt ♥ rt ♣r♦♣♦ss t♦ s ♠①tr ♦ ♦♣s t♦ ♣r♦r♠ str ♥②ss t♦rsst② r♥t ♦♣s ♠♦♥ t♠ t ss♥ ♦♣s r ♦♥sr r♠♦ s ♦s t♦ t ♣♣r♦ ♦♣ ♥ ts ♣tr ♦r t♦ ♠♣♦rt♥tr♥s t♦ ♠♥t♦♥ rst② ♣r♦♣♦s ②s♥ ♥r♥ tt♦rs ♣r♦♣♦s ♥ ♣♣r♦ ② ♠①♠♠ ♦♦ ♥r ♦♥str♥ts ♦♥②s♦♠ s③t♦♥s t♦♦s r ♣rs♥t r

trtr ♦ ts ♣tr s ♣♣r s ♦r♥③ s ♦♦s t♦♥ ♣rs♥ts t ♠①tr ♠♦ ♦ ss♥ ♦♣s ♥tr♦ t♦ str ts ♥s tt ①st♥ ♠♦s ♥ ts ♦♥trt♦♥ t♦ t s③t♦♥ ♦ ♠① rs t♦♥ s ♦t t♦ t ♣r♠tr st♠t♦♥ ♥ ②s♥ r♠♦r s♥ t♠①♠♠ ♦♦ st♠t s ♥tt♥ ❬P❪ t♦♥ strts t♦r ♦ t ♦rt♠ ♣r♦r♠♥ t ♥r♥ ♥ s♦ t ♠♦ r♦st♥ss ♦♥♥♠r ①♣r♠♥ts t♦♥ ♣rs♥ts tr ♣♣t♦♥s ♦ t ♥ ♠①tr♠♦ ② str♥ tr r t sts t♦♥ ♦♥s ts ♦r tsrsts r ♣rt ♦ t rt ♦s str♥ ♦ ss♥ ♦♣s ♦r ♠①t ❬❱❪

Page 176: Model-based clustering for categorical and mixed data sets

①tr ♠♦ ♦ ss♥ ♦♣s

①tr ♠♦ ♦ ss♥ ♦♣s

♥t ♠①tr ♠♦

t

t♦r ♦ e ♠① rs s ♥♦t ② xi = (x1i , . . . , xei ) ∈ R

c × X te = c+ d ts rst c ♠♥ts r t st ♦ t ♦♥t♥♦s rs ♥ ♦♥ ts♣ R

c ♥ rtr ♥♦t ② x

i ts st d ♠♥ts r t st ♦ t srtrs ♥tr ♦r♥ ♦r ♥r② ♥ ♦♥ t s♣ X ♥ rtr ♥♦t ②x

i ♦t tt xji s ♥ ♦r♥ r t mj ♠♦ts t♥ t ss ♥♠r

♦♥ 1, . . . ,mj

♦tt♦♥ ❲ r♠♥ tt s t ♥r ♥♦tt♦♥ P (.; .) ♦r t ♠tstrt♦♥ ♥t♦♥s ♥ p(.; .) ♦r t ♣r♦t② strt♦♥ ♥t♦♥ ♣

Pr♦t② strt♦♥ ♥t♦♥

♥t♦♥ ♥t ♠①tr ♠♦ ♦ ♣r♠tr strt♦♥s t xi rs♣♣♦s t♦ r♥ ② t ♠①tr ♠♦ ♦ g ♣r♠tr strt♦♥s ♦s t♣ s rtt♥ s ♦♦s

p(xi;θ) =

g∑

k=1

πkp(xi;αk),

r θ = (π,α) ♥♦ts t ♦ ♣r♠trs t♦r π = (π1, . . . , πg)r♦♣s t ♣r♦♣♦rt♦♥s ♦ ss k ♥♦t ② πk ♥ rs♣t♥ t ♦♦♥♦♥str♥ts 0 < πk ≤ 1 ♥

∑gk=1 πk = 1 t t♦r α = (α1, . . . ,αg) r♦♣s

t ♣r♠trs ♦ ss k ♥♦t ② αk

Pr♦♣rt② t♥t r ♥t ♠①tr ♠♦ ♥ ①♣rss ② s♥t t♥t r zi s t♦r r ♥ts t ss ♠♠rs♣② s♥ ♦♠♣t s♥t ♦♥ ♥ ♦♦s t ♠t♥♦♠ strt♦♥Mg(π1, . . . , πg) s ♥ ♥tr♣rt s t ♠r♥ strt♦♥ ♦ xis ♦♥ t strt♦♥ ♦ t ♦♣ (xi, zi)

ss♥ ♦♣ ♦r ♠① t

♦♠♣♦♥♥t strt♦♥s ♦♦♥ ss♥ ♦♣s

♦♣s ♦ t♦ ♠trt ♠♦ ② stt♥ ♦♥ t ♦♥ ♥ t♦♥♠♥s♦♥ ♠r♥s ♥ ♦♥ t ♦tr ♥ t ♣♥♥② ♠♦ t♥rs ❲ ♥♦ ♣rs♥t t ♠r♥ strt♦♥ ♦ t ♦♠♣♦♥♥ts t♥ ♦s♦♥ t ss♥ ♦♣ s ♦ ♥trst ♦r s s♥ t ♣r♦s ♦♥ ♦rrt♦♥♦♥t ♣r ♦♣ ♦ rs ♥ s♥ t ♦s ♥ s② ♣r♠tr st♠t♦♥

Page 177: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t

♥♠♥s♦♥ ♠r♥s ♦ t ♦♠♣♦♥♥ts

♦r ♦♠♣♦♥♥t ss♠ tt t ♠r♥ strt♦♥s ♦ ♦♠♣♦♥♥t♦♥s t♦ t ①♣♦♥♥t ♠② ♥ ♦rr t♦ ♣r♦ ♠♥♥ sss

♥t♦♥ ♥♠♥s♦♥ ♠r♥s ♦ t ♦♠♣♦♥♥ts ♠r♥ strt♦♥ ♦ t r xji ♦r ♦♠♣♦♥♥t k ♦♥s t♦ t ①♣♦♥♥t ♠② ♥s p(xji ;βkj) ♦r ♣ ♥ P (x

ji ;βkj) s ♦r ♣rs②

xji s ♦♥t♥♦s ts ♠r♥ ♦ ♦♠♣♦♥♥t k ♦♦s ss♥ strt♦♥t ♠♥ µkj ♥ r♥ σ2

kj xji |zik = 1 ∼ N1(µkj, σ2kj) ♥ βkj =

(µkj, σ2kj) ∈ R× R

+∗ xji s ♥tr ts ♠r♥ ♦ ♦♠♣♦♥♥t k ♦♦s P♦ss♦♥ strt♦♥

xji |zik = 1 ∼ P(βkj) ♥ βkj ∈ R+∗

xji s ♦r♥ ts ♠r♥ ♦ ♦♠♣♦♥♥t k ♦♦s ♠t♥♦♠ strt♦♥ xji |zik = 1 ∼ Mmj

(βkj) βkj ♥ ♥ ♦♥ t s♠♣① ♦ s③ mj

♣♥♥② ♠♦ ♦ t ♦♠♣♦♥♥ts

♠①tr ♠♦ ♦ ss♥ ♦♣s ss♠s tt ♦♠♣♦♥♥t k ♦♦s ss♥ ♦♣ ♦s t ♦rrt♦♥ ♠tr① ♦ s③ e× e s ♥♦t ② Γk ❲♥♦t Φe(.;Γk) t ♦ t ert ♥tr ss♥ strt♦♥ t ♦rrt♦♥♠tr① Γk ♥ Φ−1

1 (.) t ♥rs ♠t strt♦♥ ♥t♦♥ ♦N1(0, 1) s ♦t♥ t ♦♦♥ ♥t♦♥ ♦ t ♦♠♣♦♥♥t

♥t♦♥ ♠t strt♦♥ ♥t♦♥ ♦ t ♦♠♣♦♥♥ts ♦r t ♠①tr ♠♦ ♦ ss♥ ♦♣s t ♦ ♦♠♣♦♥♥t k s rtt♥ s

P (xi;αk) = Φe(Φ−11 (u1k), . . . ,Φ

−11 (uek);0,Γk),

r ujk = P (xji ;βkj) ♥ r αk = (βk,Γk) ♥♦ts t ♦ ♣r♠trs ♦♦♠♣♦♥♥t k t βk = (βk1, . . . ,βke)

Pr♦♣rt② t♥r③ ♦♥t ♦ ♦rrt♦♥ ♣r ss ss♥♦♣ ♣r♦s ♦♥t ♦ ♦rrt♦♥ ♣r ♦♣ ♦ rs s ♦♦♣r♦♣rts ♥ ♥ ♦t rs r ♦♥t♥♦s t s q t♦ t ♣♣r♦♥ ♦ t ♦♥ts ♦ ♦rrt♦♥ ♦t♥ ② t ♠♦♥♦t♦♥ tr♥s♦r♠t♦♥s♦ t rs ❬❲❪ rtr♠♦r ♥ ♦t rs r srt t s qt♦ t ♣♦②♦r ♦♥t ♦ ♦rrt♦♥ ❬s❪

Pr♦♣rt② ♦♥ t♥t r ♠①tr ♠♦ ♦ ss♥ ♦♣s♥♦s s♦♥ t♥t r t♦ t ss ♠♠rs♣ ♦♥ssts♥ ♥ ert ♦♥t♥♦s r ♥♦t ② yi = (y1i , . . . , y

ei ) ∈ R

e ♦♥t♦♥② ♦♥ t ss ♠♠rs♣ ts r ♦♦s ♥ ert ♥tr ss♥strt♦♥ ♥ yi|zik = 1 ∼ Ne(0,Γk) ♥

xji = P−1(Φ1(yj);βkj), ∀j = 1, . . . , e,

t♥ ♦♠♣♦♥♥t k s ss♥ ♦♣ ♦s t s P (xi;αk)

Page 178: Model-based clustering for categorical and mixed data sets

①tr ♠♦ ♦ ss♥ ♦♣s

①tr ♠♦ ♦ ss♥ ♦♣s ♦r ♠① t

❲ ♥tr♦ t ♥t♦♥ Ψ(x

i ;αk) =(xji−µkj

σkj; j = 1, . . . , c

)

♥ t s♣ ♦

t ♥t♥ts ♦ x

i ♦r ss k s ♥♦t Sk(x

i ) = Sc+1k (xc+1

i )× . . .× Sek(xei ) ♥tr Sjk(xji ) =]b⊖k (x

ji ), b

⊕k (x

ji )] s ♥ ♦r j = c + 1, . . . , e ♥ ts ♦♥s r

b⊖k (xji ) = Φ−1

1 (P (xji − 1;βkj)) ♥ b⊕k (x

ji ) = Φ−1

1 (P (xji ;βkj)) ❲ ♥♦ ♥ t ♣♦ t ♦♠♣♦♥♥ts ♦r♥ t♦ s ♣r♦♣♦s ♥ ❬❪

♥t♦♥ ①tr ♠♦ ♦ ss♥ ♦♣s t xi ♦♦s ♠①tr♠♦ ♦ ss♥ ♦♣s ts ♣ s t ♥t ♠①tr ♠♦ ♥ ♥ ♦s t ♣ ♦ ♦♠♣♦♥♥t k s rtt♥ s

p(xi;αk) = p(x

i ;αk)p(x

i |x

i ;αk)

=φc(Ψ(x

i ;αk);0,Γk)∏c

j=1 σkj

Sk(x

i )

φd(u;µ

k ,Σ

k)du,

r Γk =

[

Γk Γk

Γk Γk

]

s ♦♠♣♦s ♥t♦ s♠trs ♦r ♥st♥ Γk s t

s♠tr① ♦ Γk ♦♠♣♦s ② t r♦s ♥ t ♦♠♥s rt t♦ t ♦sr♦♥t♥♦s rs ♦r♦r µ

k s t ♦♥t♦♥ ♠♥ ♦ yi ♥ ② µ

k =ΓkΓ

−1kΨ(x

i ;αk) ♥ Σ

k s ts ♦♥t♦♥ ♦r♥ ♠tr① ♥ ② Σ

k =Γk − ΓkΓ

−1kΓk

Pr♦♣rt② ♥rt ♠♦ ♠①tr ♠♦ ♦ ss♥ ♦♣s ♥♦st ♥rt ♠♦ s♣t ♥t♦ t ♦♦♥ tr st♣s

ss ♠♠rs♣ s♠♣♥ zi ∼ Mg(π1, . . . , πg) ss♥ ♦♣ s♠♣♥ yi|zik = 1 ∼ Ne(0,Γk) sr t tr♠♥st ♦♠♣tt♦♥ xi s ♦t♥ r♦♠

♠rs

♦♠♦sst ♠♦s ❲♥ t s♠♣ s③ s s♠ t tr ♦ t♥t s ♥ t r♥ ♦ t st♠t ♠② ttr s♦♠ ♦♥str♥ts♦♥ t ♣r♠tr s♣ r s ♣r♦♣♦s ♣rs♠♦♥♦s rs♦♥♦ t ♠①tr ♠♦ ♦ ss♥ ♦♣s ② ss♠♥ t qt② t♥t ♦rrt♦♥ ♠trs s♦

Γ1 = . . . = Γg.

♦t tt ts ♠♦ s ♥♠ ♦♠♦sst s♥ t ♦r♥ ♠trs♦ t t♥t ss♥ rs r q t♥ sss

♠r ♦ ♣r♠trs tr♦sst rs♣t② ♦♠♦sst♠①tr ♠♦ ♦ ss♥ ♦♣s ♥s ν rs♣t② ν♦ ♣r♠trsr

ν = (g−1)+g

(

e(e− 1)

2+

d∑

j=1

νj

)

♥ ν♦ = (g−1)+e(e− 1)

2+g

d∑

j=1

νj,

Page 179: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t

r νj ♥♦ts t ♥♠r ♦ ♣r♠trs ♦ t ♠r♥ strt♦♥ ♦r j ♦r ♦♥ ♦♠♣♦♥♥t ♦r ♣rs② t t s♣ ♠r♥ strt♦♥ ♦ t ♦♠♣♦♥♥ts νj s q t♦

νj =

2 xj s ♥♠r1 xj s srt

mj − 1 xj s ♦r♥

♦ ♥tt② ♠①tr ♠♦ ♦ ss♥ ♦♣s s ♥t♥ t s♥s ♥ ♥ ❬ ❨❪ t st ♦♥ r s ♦♥t♥♦s♦r ♥tr ♣r♦♦ s ♥ ♥ ♣♣♥①

tr♥ts ♦ t ♠①tr ♠♦

t ♠♦s

♠①tr ♠♦ ♦ ss♥ ♦♣s ♦s t♦ ♥r③ ♠♥② ss♠♦s str♥s ♠♦♥ t♠ ♦♥ ♥ t t ♦♦♥ ♦r

♦s② t ♦rrt♦♥ ♠trs r ♦♥ Γk = I ∀k =1, . . . , g t♥ t ♠①tr ♠♦ ♦ ss♥ ♦♣s s q♥t t♦ t♦♥t♦♥ ♥♣♥♥ ♠①tr ♠♦

t rs r ♦♥t♥♦s c = e ♥ d = 0 t♥ t ♠①tr♠♦ ♦ ss♥ ♦♣s ♦♠s ♠trt ss♥ ♠①tr ♠♦t♦t ♦♥str♥t t♥ t ♣r♠trs ❬❪

♠①tr ♠♦ ♦ ss♥ ♦♣s s ♥ t♦ t ♥♥ ss♥♠①tr ♠♦ ♦r ♥st♥ t s q♥t ♥ t r ♦r♥ t♦ t♠①tr ♠♦ ♦ ❬♦❪ ♥ s s ts ♠♦ s st ② s♦♥ ♦♠♦ts

❲♥ t rs r ♦t ♦♥t♥♦s ♥ ♦r♥ t ♠①tr ♠♦ ♦ss♥ ♦♣s s ♥ ♣r♠tr③t♦♥ ♦ t ♠①tr ♠♦ ♣r♦♣♦s② rtt ❬❪ s t♦♥ ♦r rtt st♠ts rt② ts♣ Sk(x

i ) ♦♥t♥♥ t ♥t♥ts ♦ x

i ♥ ♥♦t t ♠r♥ ♣r♠trss t ♠①♠♠ ♦♦ ♥r♥ s s♦ ♣r♦r♠ s♠♣① ♦rt♠ r♠t② ♠t♥ t ♥♠r ♦ ♦r♥ rs ♦t tt ♦r♣♣r♦ ♦r t ♥r♥ ♦s ts r s ts ♥ t♦♥

t s③t♦♥ ♣r ss ②♣r♦t ♦ ss♥ ♦♣s

❲ ♥ s t ♠♦ ♣r♠trs t♦ ♦t♥ s③t♦♥ ♦ t ♥s ♣rss ♥ t♦ r♥ ♦t t ♠♥ ♥trss ♣♥♥s s ♦r ss k rst②♦♠♣t t ♦♦r♥ts q t♦ E[yi|xi, zik = 1;αk] ♥ s♦♥② ♣r♦t t♠♦♥ t ♣r♥♣ ♦♠♣♦♥♥t ♥②ss s♣ ♦ t ss♥ ♦♣ ♦ ♦♠♣♦♥♥t k♦t♥ ② t s♣tr ♦♠♣♦st♦♥ ♦ Γk

♥s r♥ ② t ♦♠♣♦♥♥t k ♦♦ ♥tr ss♥ strt♦♥♥ t t♦r ♠♣ s♦ t② r ♦s t♦ t ♦r♥ ♦s r♥ ② ♥♦tr♦♠♣♦♥♥t ♥ ①♣tt♦♥ r♥t r♦♠ ③r♦ s♦ t② r rtr t♦ t ♦r♥♥② t ♦rrt♦♥ r s♠♠r③s t ♥trss ♦rrt♦♥s ♦♦♥①♠♣ strts ts ♣♥♦♠♥♦♥

Page 180: Model-based clustering for categorical and mixed data sets

①tr ♠♦ ♦ ss♥ ♦♣s

①♠♣ ①tr ♠♦ ♦ ss♥ ♦♣s ♥ s③t♦♥ ♣r ss tt ♦♠♣♦♥♥t ♠①tr ♠♦ ♦ ss♥ ♦♣s ♦♠♣♦s t tr rs♦♥ ♦♥t♥♦s ♦♥ ♥tr ♥ ♦♥ ♥r② ♥ ts ♦rr t

π = (0.5, 0.5), β11 = (−2, 1), β12 = 5, β13 = (0.5, 0.5),β21 = (2, 1), β22 = 15,

β23 = (0.5, 0.5), Γ1 =

1 −0.4 0.4−0.4 1 0.40.4 0.4 1

♥ Γ2 =

1 0.8 0.10.8 1 0.10.1 0.1 1

.

−4 −2 0 2 4

510

1520

25

x1

x2

−6 −4 −2 0 2

−2

−1

01

23

first principal component axis

seco

nd p

rinci

pal c

ompo

nent

axi

s

−1.0 −0.5 0.0 0.5 1.0

−1.

0−

0.5

0.0

0.5

1.0

inertia: 60.8 %

iner

tia: 3

2.5

%

continuous

integer

binary

r ①♠♣ ♦ s③t♦♥ sttr♣♦t ♦ t ♥s sr② tr rs ♦♥ ♦♥t♥♦s sss ♦♥ ♥tr ♦r♥t ♥ ♦♥ ♥r②s②♠♦ ♥s sttr♣♦t ♥ t rst ♦♠♣♦♥♥t ♠♣ ♦ ss rs r♣rs♥tt♦♥ ♥ t rst ♦♠♣♦♥♥t ♠♣ ♦ ss ♦♦r ♥tst ss ♠♠rs♣s

s③t♦♥ ♦ ss s ♣rs♥t ♥ r ♦♥r♥♥ t ♥st sttr♣♦t s♦s ♥tr ss t r ♦♥ ♥ s♦♥ ss t ♦♥ ♦t ♦♥ t t s ♦♥r♥♥ t rs t r♣rs♥tt♦♥ ♣♦♥ts ♦t② str♦♥ ♥trss ♦rrt♦♥ t♥ t ♦♥t♥♦s ♥ t ♥tr rs

Page 181: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t

②s♥ ♥r♥ tr♦♣♦st♥s

s♠♣r

♠ ❲ ♦sr t s♠♣ ① = (x1, . . . ,xn) ♦♠♣♦s t n ♥♣♥♥t ♥s xi ∈ R

c×X ss♠ t♦ r♥ ② ♠①tr ♠♦ ♦ ss♥ ♦♣s ♠ s t♦ ♥r t ♣r♠trs ♦r♥ t♦ t t

rq♥tst ♦♥t①t ♥r♥ ② ♠①♠♠ ♦♦ s t ♣r♦♠♦r t ♣r♠tr ♦♣s ♥ t ♠r♥ ♣r♠trs r ♥♥♦♥ ♦ ts ♦t♥ r♣ ② t ♥r♥ ♥t♦♥ ♦r r♥s ♠t♦ ♣r♦r♠♥ t♥r♥ ♥ t♦ st♣s s ♣tr ♦ ❬♦❪ rst st♣ st♠ts t♠r♥ ♣r♠trs ② ♠①♠③♥ ♥rt ♦♦ t s♦♥ st♣st♠ts t ♦rrt♦♥ ♣r♠trs ② ♠①♠③♥ t ♦♦ ♦♥t♦♥② ♦♥t ♠r♥ ♣r♠trs s ♣♣r♦ s s ♥ ❬❪ ♦r t ♠①♠♠♦♦ st♠t ♥ ss♥t② ♦t♥ ♥ t rs r ♦♥t♥♦s② s♥ t ①♣♦♥t ♦rt♠ ♣r♦♣♦s ② ❬❪ ♥ ts ♣♣r♦ ♥♥♦t ①t♥ t♦ t ♠① t stt♥ s ♥ ♠ ♦rt♠ ♥ ♥♦t ♠♣♠♥t t♦ ♦t♥ t ♠①♠♠ ♦♦ st♠ts ♦ ♠①tr ♠♦ ♦ss♥ ♦♣s ♥ t ♠① t s rtr♠♦r ♥ t ♠ st♣ ♦ ①♣t t st♣ ♦ t♦♦ ♠ t♠ ♦♥s♠♥ t srt rsr ♥♠r♦s s ♦ t ♦♠♣tt♦♥ ♦ t ♥tr ♦ ♠♥s♦♥ d ♥ ♥

②s♥ ♦♥t①t ♥ ♦rr t♦ ♦ ♦t ♣r♦s ♣r♦♠s ♣rr t♦ ♦r♥ ②s♥ r♠♦r ❲ rst② ♥ t ♣r♦r strt♦♥s ♥ s♦♥②♣rs♥t t s s♠♣r ♣r♦r♠♥ t ♥r♥

①♠♠ ♣♦str♦r st♠t

Pr♦r strt♦♥s

♥♣♥♥ ss♠♣t♦♥ ss ss♠♣t♦♥ s t♦ s♣♣♦s t ♥♣♥♥ t♥ t ♣r♦r strt♦♥s ts

p(θ) = p(π)

g∏

k=1

(

p(Γk)d∏

j=1

p(βkj)

)

.

Pr♦♣♦rt♦♥s ss ♦♥t ♣r♦r strt♦♥ ♦ t ♣r♦♣♦rt♦♥ t♦rs t r②s ♥♦♥ ♥♦r♠t ♦♥ s rt strt♦♥ ♦s t ♣r♠trs r q t♦

π ∼ Dg

(

1

2, . . . ,

1

2

)

.

r♥ ♣r♠trs ♣r♦r strt♦♥ ♦ t ♠r♥ ♣r♠trs r tss ♦♥t ♦♥s ♦r ♣rs②

Page 182: Model-based clustering for categorical and mixed data sets

②s♥ ♥r♥ tr♦♣♦st♥s s♠♣r

xj s ♦♥t♥♦s t♥ βkj ♥♦ts t ♣r♠trs ♦ ♥rt ss♥strt♦♥ s♦ p(βkj) = p(µkj|σ2

kj)p(σ2kj) t

σ2kj ∼ G−1(c0, C0) ♥ µkj|σ2

kj ∼ N1(b0, σ2kj/N0),

r G−1(., .) ♥♦ts t ♥rs ♠♠ strt♦♥ ❲t ♥ ♠♣r②s♥ ♣♣r♦ t ②♣r♣r♠trs (c0, C0, b0, N0) r ① s ♣r♦♣♦s ② ❬❪ s♦ c0 = 1.28 C0 = 0.36❱r(①j) b0 = 1

n

∑ni=1 x

ji ♥

N0 =2.6

r♠① ①j−r♠♥ ①j xj s ♥tr βkj ♥♦ts t ♣r♠tr ♦ P♦ss♦♥ strt♦♥ ♥

βkj ∼ G(a0, A0).

♦r♥ t♦ ❬❪ t s ♦ ②♣r♣r♠trs a0 ♥ A0 r ♠♣r②① t♦ a0 = 1 ♥ A0 = a0n/

∑ni=1 x

ji

xj s ♦r♥ βkj ♥♦ts t ♣r♠tr ♦ ♠t♥♦♠ strt♦♥ ♥ts r②s ♥♦♥ ♥♦r♠t ♦♥t ♣r♦r ♥♦s tt

βkj ∼ Dmj

(

1

2, . . . ,

1

2

)

.

♦rrt♦♥ ♠trs ♦♥t ♣r♦r ♦ ♦r♥ ♠tr① s t ♥rs❲srt strt♦♥ ♥♦t ② W−1(., .) ♦ t s ♥tr t♦ ♥ t ♣r♦r ♦t ♦rrt♦♥ ♠tr① Γk r♦♠ t ♣r♦r ♦ t ♦rrt♦♥ ♠tr① Λk s♥ Γk|Λk str♠♥st ❬♦❪ ♦

Λk ∼ W−1(s0, S0) ♥ ∀1 ≤ h, ℓ ≤ e, Γk[h, ℓ] =Λk[h, ℓ]

Λk[h, h]Λk[ℓ, ℓ],

r (s0, S0) r t♦ ②♣r♣r♠trs ♦r t ss ♣♣r♦ ♦♥sst♥♥ tt♥ t ②♣r♣r♠trs tr♦ ♥ ♠♣r ②s♥ ♣♣r♦ s ♥♦t ♣♦ss s♥ yi s ♥♦t ♦sr ❲ ts ♣t s0 = e + 1 ♥ S0 q t♦ t ♥tt②♠tr① s♥ ♥ ts s t ♠r♥ strt♦♥ ♦ ♦rrt♦♥ ♦♥t s♥♦r♠ ♦♥ ]− 1, 1[ ❬❪

P♦str♦r strt♦♥

②s♥ ♥r♥ s ♣r♦r♠ ② s♠♣♥ sq♥ ♦ ♣r♠trs r♦♠tr ♣♦str♦r strt♦♥ ♥ ♣rt s s s♠♣r s t ♠♦st♣♦♣r ♣♣r♦ t♦ ♣r♦r♠ ②s♥ ♥r♥ ♦ ♠①tr ♠♦ s♥ t ss tt♥t strtr ♦ t t ♥ t tr♥t② s♠♣s t ss ♠♠rs♣s♦♥t♦♥② ♦♥ t ♣r♠trs ♥ ♦♥ t t ♥ t ♣r♠trs ♦♥t♦♥② ♦♥ t ss ♠♠rs♣s ♥ ♦♥ t t ♥ ts stt♦♥r② strt♦♥s p(θ, ③|①) t sq♥ ♦ t ♥rt ♣r♠trs s r♥ ② t ♠r♥♣♦str♦r strt♦♥ p(θ|①) s ♦rt♠ rs ♦♥ t♦ ♥str♠♥t rst ss ♠♠rs♣ ♦ t ♥s ♦ ① ♥♦t ② ③ = (z1, . . . , zn) ♥ tss♥ t♦r ♦ t ♥s ♥♦t ② ② = (y1, . . . ,yn)

Page 183: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t

s s♠♣r

trt♥ r♦♠ ♥ ♥t θ[0] ts trt♦♥ [r] s rtt♥ s

③[r],②[r−1/2] ∼ ③,②|①,θ[r−1]

β[r]kj ,y

j[r][rk] ∼ βkj,y

j[rk]|①,y

[r][rk], ③

[r],β[r]k ,Γ

[r−1]k

π[r] ∼ π|③[r]

Γ[r]k ∼ Γk|②[r], ③[r],

♦rt♠ s s♠♣r

r y[rk] = yi:z

[r]i =k

y[r]i = (y1[r]i , . . . , y

j−1[r]i , y

j+1[r−1/2]i , . . . , y

e[r−1/2]i ) ♥ β

[r]k =

(β[r]k1, . . . ,β

[r]kj−1,β

[r−1]kj+1, . . . ,β

[r−1]ke )

♠r s♠♣♥ ♦ t ss♥ r ss♥ r② s t ♥rt r♥ ♦♥ trt♦♥ ♦ t s s♠♣r t ♦♦s② tsstt♦♥r② strt♦♥ st②s ♥♥ s t s♠♣♥ s ♠♥t♦r② s♦ t str♦♥ ♣♥♥② t♥ ② ♥ ③ ♥ t♥ y

j[rk] ♥ βkj

♠r ♥ t tr♦♣♦st♥s s♠♣r t s♠♣♥s r♦♠ ♥ r ss t t♦ ♦tr ♦♥s r ♠♦r ♦♠♣① ♥ ts♠♣♥ r♦♠ ♥♦s t♦ ♦♠♣t t ♦♥t♦♥ ♣r♦ts ♦ t ss♠♠rs♣s s♦ t♦ ♦♠♣t t ♥tr ♥ ♥ t ♥♠r ♦ srt rs s r ts ♦♠♣tt♦♥ s t♠ ♦♥s♠♥ ♦r t s♠♣♥ r♦♠ ♥ ♥t② ♣r♦r♠ ② ♦♥ trt♦♥ ♦ tr♦♣♦sst♥s ♦rt♠ ♥ p(zi,yi|xi, t(r−1)) s stt♦♥r② strt♦♥ ♦♥r♥♥t s♠♣♥ ♦r♥ t♦ t s ♣r♦r♠ ♥ t♦ st♣s rst② t ♠r♥♣r♠tr s s♠♣ ② ♦♥ trt♦♥ ♦ tr♦♣♦sst♥s ♦rt♠ ♥p(βkj|①,y↑j(r)

[rk] , ③(r),β

↑j(r)k ,Γk) s stt♦♥r② strt♦♥ ♦♥② t t♥t s

s♥ t♦r s s♠♣ r♦♠ ts ♦♥t♦♥ strt♦♥

♠r s♠♣♥ ♦ t ss♥ r ss♥ r② s t ♥rt r♥ ♦♥ trt♦♥ ♦ t s s♠♣r t ♦♦s② tsstt♦♥r② strt♦♥ st②s ♥♥ s t s♠♣♥ s ♠♥t♦r② s♦ t str♦♥ ♣♥♥② t♥ ② ♥ ③ ♥ t♥ y

j[rk] ♥ βkj

❲ ♥♦ t t ♦r st♣s ♦ t s s♠♣r ♥ ♣♦♥t ♦t t tst♦ s♠♣ r♦♠ ♥ s ♦t st♣s r ♠♦ t♦ ♦t♥ ttr♦♣♦st♥s s♠♣r t ♥ t ♥①t st♦♥

ss ♠♠rs♣ ♥ ss♥ t♦r s♠♣♥

♠ s t♦ s♠♣ r♦♠ ② s♥ t ♥♣♥♥ t♥ t ♥s t t♦rs (③,②) r s② s♠♣ ♦♥t♦♥② ♦♥ (①,θ[r−1]) ♦r♥t♦

p(③,②|①,θ[r−1]) =n∏

i=1

p(zi|xi,θ[r−1])p(yi|xi, zi,θ[r−1]).

Page 184: Model-based clustering for categorical and mixed data sets

②s♥ ♥r♥ tr♦♣♦st♥s s♠♣r

❲ ♥♦ t ♦t strt♦♥s ♦ t rt s ♦ t ♦ qt♦♥ z

[r]i s ♥♣♥♥t② s♠♣ r♦♠ t ♦♦♥ ♠t♥♦♠ str

t♦♥zi|xi,θ[r−1] ∼ Mg(ti1(θ

[r−1]), . . . , tig(θ[r−1])),

r tik(θ[r−1]) =

π[r−1]k p(xi;α

[r−1]k )

p(xi;θ[r−1])

s t ♣♦str♦r ♣r♦t② tt xi s ♥

r♥ ② ♦♠♣♦♥♥t k t t ♣r♠trs θ[r−1] y

[r−1/2]i s ♥♣♥♥t② s♠♣ ② r♠r♥ tt t rst c ♠♥ts

♦ yi ♥♦t ② yi r tr♠♥st ♦r ① tr♣t (xi, zi,θ[r−1]) t

zik = 1 s s yi = Ψ(x

i ;α[r−1]k ) ts st d ♠♥ts ♥♦t ②

yi r s♠♣ ♦r♥ t♦ drt ss♥ strt♦♥ Nd(0,Γ[r−1]k )

tr♥t ♦♥ t s♣ Sk(x

i )

p(yi |xi, zi,θ[r−1]) ∝g∏

k=1

(

φd(y

i ;µ[r−1]k ,Σ

[r−1]k )1yi ∈Sk(x

i )

)zik,

r µ[r−1]k = Γ

[r−1]k Γ

−1[r−1]k Ψ(x

i ;α[r−1]k )

♠r ts t♦ ♦♠♣t tik(θ[r−1]) ♦t tt t ♦♠♣tt♦♥ ♦

tik(θ[r−1]) ♥♦s t♦ ♦♠♣t t ♥tr ♥ ♥ ♥ t♦♦ ♠

t♠ ♦♥s♠♥ d s r d > 6 s t s♠♣♥ ♦r♥ t♦ ss♦ ♣r♦r♠ ② ♦♥ trt♦♥ ♦ tr♦♣♦sst♥s ♦rt♠ ♦♥ tst② ♥ t ♥ t ♥①t st♦♥

r♥ ♣r♠tr ♥ ss♥ t♦r s♠♣♥

♠ s t s♠♣♥ r♦♠ ♥ ♦♠♣♦s s ♦♦s

p(βkj,yj[rk]|①,y

[r][rk], ③

[r],β[r]k ,Γ

[r−1]k ) = p(βkj|①,y[r][rk], ③

[r],β[r]k ,Γ

[r−1]k )

× p(yj[rk]|①,y[r][rk], ③

[r],β[r]k ,βkj,Γ

[r−1]k ).

❲ ♥♦ t ♦t strt♦♥s ♦ t rt s ♦ t ♦ qt♦♥ ♦♥t♦♥ strt♦♥ ♦ βkj s ♥ t ♥ ♥♥♦♥ ♥tr♣t

s s

p(βkj |①,y[r][rk], ③[r],β

[r]k ,Γ

[r−1]k ) ∝ p(βkj)

n∏

i=1

(

p(xji |y↑j[r]i , z

[r]i ,Γ

[r−1]k ,βkj)

)z[r]ik

.

♦♥t♦♥ strt♦♥ ♦ xji |y↑j[r]i , z

[r]i ,Γ

[r−1]k t z[r]ik = 1 s ♦♥ t

rt s ♦ t ♦ qt♦♥ s ♥ ②

p(xji |y↑j[r]i , z

[r]i ,Γ

[r−1]k ,βkj) =

φ1(xji−µkjσkj

; µi, σ2i )/σkj 1 ≤ j ≤ c

Φ1(b⊕(xji )−µi

σi)− Φ1(

b⊖(xji )−µiσi

) ♦trs,

r t r µi = Γ[r−1]k [j, ]Γ

[r−1]k [, ]−1y

↑j[r]i s t ♦♥t♦♥ ♠♥ ♦

yji Γk[j, ] ♥ t r♦ j ♦ Γk ♣r ♦ t ♠♥t j ♥ Γk[, ] ♥

Page 185: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t

t ♠tr① Γk ♣r ♦ t r♦ ♥ t ♦♠♥ j ♥ r σ2i s t

♦♥t♦♥ r♥ ♦ yji ♥ ② σ2i = 1−Γ

[r−1]k [j, ]Γ

[r−1]k [, ]−1

Γ[r−1]k [, j]

② t ♥♣♥♥ t♥ t ♥s t ♦♥t♦♥ strt♦♥♦ yj[rk] s ①♣t② ♥ s

p(yj[rk]|①,y[r][rk], ③

[r],β[r]k ,βkj,Γ

[r−1]k ) =n∏

i=1

(

p(yji |xji ,y↑j[r]i , z

[r]i ,βkj,Γ

[r−1]k )

)z[r]ik

.

xj s ♦♥t♥♦s r 1 ≤ j ≤ c ♥ z[r]i = k t

♦♥t♦♥ strt♦♥ ♦ yji s tr♠♥st s s

yj[r]i =

xji − µ[r]kj

σ[r]kj

.

xj s srt r c + 1 ≤ j ≤ e ♥ z[r]ik = 1 t

♦♥t♦♥ strt♦♥ ♦ yji s tr♥t ss♥ strt♦♥ s s

p(yji |xji ,y↑j[r]i , z

[r]i ,β

[r]kj ,Γ

[r−1]k ) =

φ1(yji ; µi, σ

2i )

p(xji ;β[r]kj)

1yji∈[b

⊖[r]k (xji ),b

⊕[r]k (xji )]

,

r b⊖[r]k (xji ) = P (xji − 1;β

[r]kj) ♥ b

⊕[r]k (xji ) = P (xji ;β

[r]kj)

♠r ts t♦ s♠♣ t ♠r♥ ♣r♠trs s♠♣♥ ♦ βkjs ♥♦t s② ♣r♦r♠ s♥ t ♥♦r♠③♥ ♦♥st♥t ♥ ♥ s ♥♥♦♥s st♣ s t♥ r♣ ② ♦♥ trt♦♥ ♦ tr♦♣♦sst♥s ♦rt♠ st ♥ t ♥①t st♦♥ ♦r ♥♦t tt t s♠♣♥ ♦ yj[rk] r♦♠ ss② ♣r♦r♠

❱t♦r ♦ ♣r♦♣♦rt♦♥s s♠♣♥

♠ s t s♠♣♥ r♦♠ s ss ♦r t ♠①tr ♠♦ ♦♥t r②s ♥♦♥ ♥♦r♠t ♣r♦r ♥♦s tt

π|③[r] ∼ Dg

(

♥[r]1 +1

2, . . . , ♥[r]g +

1

2

)

,

r ♥[r]k =∑n

i=1 z[r]ik

♦rrt♦♥ ♠tr① s♠♣♥

♠ s t s♠♣♥ r♦♠ ❲ s t ♣♣r♦ ♣r♦♣♦s ② ❬♦❪♥ t s ♦ s♠♣r♠tr ss♥ ♦♣ s ♥t♦ t♦ st♣srst② ♦r♥ ♠tr① s ♥rt ② ts ①♣t ♣♦str♦r strt♦♥ ♥s♦♥② t ♦rrt♦♥ ♠tr① s ② ♥♦r♠③♥ t ♦r♥ ♠tr①❲♥ (②, ③) r ♥♦♥ r ♥ t ♥♦♥ s ♦ ♠trt ss♥

Page 186: Model-based clustering for categorical and mixed data sets

②s♥ ♥r♥ tr♦♣♦st♥s s♠♣r

♠①tr ♠♦ t ♥♦♥ ♠♥s s t s♠♣♥ ♦r♥ t♦ Γk|②[r], ③[r] s♣r♦r♠ ② t t♦ ♦♦♥ st♣s

Λk|②[r], ③[r] ∼ W−1

s0 + ♥[r−1]

k , S0 +∑

i:z[r]i =k

y[r]Ti y

[r]i

∀1 ≤ h, ℓ ≤ e, Γk[h, ℓ] =Λk[h, ℓ]

Λk[h, h]Λk[ℓ, ℓ].

♠r ♠♣♥ ♦ t ♦rrt♦♥ ♠trs ♦r t ♦♠♦sst ♠♦s t ♦♠♦sst ♠♦ ss♠s t qt② t♥ t ♦rrt♦♥ ♠trs♥ s s ♦♥② s♠♣ ♦♥ Λ s♦ s r♣ ②

Λ|②[r], ③[r] ∼ W−1

(

s0 + n, S0 +n∑

i=1

y[r]Ti y

[r]i

)

,

♥ ♣t Λk = Λ ♦r k = 1, . . . , g

♦r♥ t♦ ♦t ♠rs ♥ t rst t♦ st♣s ♦ t s s♠♣r♥♦ ts ♦ ② t ♦♦♥ ②r ♠♠ ♦rt♠

tr♦♣♦st♥s s♠♣r

❲♥ s♦♠ st♣s ♦ s s♠♣r ♥♥♦t s② s♠t t ♠② st♦ ♣r♦r♠ t ♥r♥ ②r ♠♠ ♦rt♠ ❬❪ s s ttr♦♣♦st♥s s♠♣r r♣s ♦t s♠♣♥ r♦♠ ③,②|①,θ[r−1]

♥ βkj|①,y[r][rk], ③[r],β

[r]k ,Γ

[r−1]k ♥ ② ♥ ② ♦♥ trt♦♥ ♦ t♦

tr♦♣♦sst♥s st♣s tt ♥♦ t

ss ♠♠rs♣ ♥ ss♥ t♦r s♠♣♥

st♣ s ♣r♦r♠ ♦♥ trt♦♥ ♦ t tr♦♣♦sst♥s ♦rt♠ s ♦rt♠ s ♥♣♥♥t② ♣r♦r♠ t♦ s♠♣ ♦♣ (zi,yi)s♥ t ♥s r ♥♣♥♥t ts stt♦♥r② strt♦♥ s

p(zi,yi|xi,θ[r−1]) ∝g∏

k=1

(

π[r−1]k φe(yi;0,Γ

[r−1]k )1

yi=Ψ(xi ;α[r−1]k )

1yi ∈Sk(x

i )

)zik.

tr♦♣♦sst♥s ♦rt♠ s♠♣s ♥t (z⋆i ,y

⋆i ) ② t ♥str

♠♥t strt♦♥ q1(.|xi,θ[r−1]) ♥♦r♠② s♠♣s z⋆i t♥ s♠♣sy⋆i |z⋆i s ♦♦s ♦♥t♦♥② ♦♥ z⋆ik⋆ = 1 ts ♥str♠♥t strt♦♥ s tr♠♥st ♦r t rst c ♠♥ts ♦ y⋆i ♥♦t ② y⋆i s s y⋆i = Ψ(x

i ;α[r−1]k⋆ )

t s♠♣s t st d ♠♥ts ♦ y⋆i ♥♦t ② y⋆i ♦r♥ t♦ ♠trt♥♣♥♥t ss♥ strt♦♥ tr♥t ♦♥ Sk⋆(x

i ) s

q1(zi,yi|xi,θ[r−1]) =

g∏

k=1

(

1

g

φd(y

i ;0, I)∏e

j=c+1 p(xji ;β

[r−1]kj )

1yi=Ψ(xi ;α

[r−1]k )

1yi ∈Sk(x

i )

)z⋆ik

.

Page 187: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t

♥t s ♣t t t ♣r♦t②

ρ[r]1i = min

∏gk=1

(

πkφe(y⋆i ;0,Γ

[r−1]k )

)z⋆ik

∏gk=1

(

πkφe(y[r−1]i ;0,Γ

[r−1]k )

)z[r−1]ik

q1(z[r−1]i ,y

[r−1]i |xi)

q1(z⋆i ,y⋆i |xi)

; 1

.

s t trt♦♥ [r] ♦ t ♦rt♠ t s♠♣♥ ♦r♥ t♦ s♣r♦r♠ ♦♥ trt♦♥ ♦ t ♦♦♥ tr♦♣♦sst♥s ♦rt♠

s ♦rt♠ s p(zi,yi|xi,θ[r−1]) s stt♦♥r② strt♦♥ ts s rtt♥ s ♦♦s

(z⋆i ,y⋆i ) ∼ q1(zi,yi|xi)

(z[r]i ,y

[r−1/2]i ) =

(z⋆i ,y⋆i ) t ♣r♦t② ρ[r]1i

(z[r−1]i ,y

[r−1]i ) t ♣r♦t② 1− ρ

[r]1i .

♦rt♠ tr♦♣♦sst♥s

r♥ ♣r♠tr s♠♣♥

st♣ s ♣r♦r♠ ♥ t♦ st♣s rst② t s♠♣♥ ♦ β[r]kj ♦r♥

t♦ s ♣r♦r♠ ♦♥ trt♦♥ ♦ t tr♦♣♦sst♥s ♦rt♠ ♦st stt♦♥r② strt♦♥ s p(βkj|①,y[r][rk], ③

[r],β[r]k ,Γk) ♦♥② t s♠♣♥ ♦

yj[r][rk] s ♣r♦r♠ ♦r♥ t♦ ts ♦♥t♦♥ strt♦♥ ♥ ②

♥str♠♥t strt♦♥ ♦ t tr♦♣♦sst♥s ♦rt♠ q2(.|①, ③) s♠♣s ♥t β⋆kj ♦r♥ t♦ t ♣♦str♦r strt♦♥ ♦ βkj ♥r t ♦♥t♦♥♥♣♥♥ ss♠♣t♦♥ ts strt♦♥ s ①♣t s♥ t ♦♥t ♣r♦r strt♦♥s r s ♦

q2(.|①, ③) = p(βkj|①, ③,Γk = I).

s ♦r♥ t♦ t ♥t β⋆kj s ♣t t t ♣r♦t②

ρ[r]2 = min

p(β⋆kj)q2(β[r−1]kj |①, ③)

p(β[r−1]kj )q2(β

⋆kj |①, ③)

i:z[r]i =k

p(yji |xji ,y

↑j[r]i , zi,β

⋆kj ,Γ

[r−1]k )

p(yji |xji ,y

↑j[r]i , zi,β

[r−1]kj ,Γ

[r−1]k )

; 1

.

s t trt♦♥ [r] ♦ t ♦rt♠ t s♠♣♥ r♦♠ s ♣r♦r♠ ♦♥ trt♦♥ ♦ t ♦♦♥ tr♦♣♦sst♥s ♦rt♠

Page 188: Model-based clustering for categorical and mixed data sets

♠r ①♣r♠♥ts ♦♥ s♠t t sts

s ♦rt♠ s p(βkj|x[rk],y[r][rk], ③,β

[r]k ,Γk) s stt♦♥r② strt♦♥ t

s rtt♥ s ♦♦s

β⋆kj ∼ q2(βkj|①, ③)

β[r]kj =

β⋆kj t ♣r♦t② ρ[r]2

β[r−1]kj t ♣r♦t② 1− ρ

[r]2 .

♦rt♠ tr♦♣♦sst♥s

♠r ♥str♠♥t strt♦♥s ♦t tt t s♠r r t ♥trss ♣♥♥s ♦ t r xi t ♦sr ♦ t stt♦♥r② strt♦♥s rt ♥str♠♥t strt♦♥s ♦ ♦t tr♦♣♦sst♥s ♦rt♠s

st♥ ♣r♦♠

st♥ ♣r♦♠ s ♥r② s♦ ② s♣ ♣r♦rs ❬t❪♦r s ♦♥ t r♠♥t ♦♣ ♥ ❬P❪ ts t♥qs r ♣r♥♣② ♠♣t♥ ♥ g s ♥♦♥

❲♥ t ♠♦ s s t♦ str t ♥♠r ♦ sss s ♥♥♦♥ ♥ t♠♦ st♦♥ s ♣r♦r♠ ② t rtr♦♥ s♠t♥♦s② ♦s t st♥ ♣♥♦♠♥♦♥ ♥ ♦♥ t ♦♥ ♥ ts rtr♦♥ sts qts♣rt sss ♥ t s♠♣ s③ s s♠ s♦ t st♥ s ♥♦t ♣rs♥t♥ ♣rt s ♦ t ss s♣rt② ♥ t ♦tr ♥ ♥ t ♥ st♠♦r sss ♥ t s♠♣ s③ ♥rss t st♥ ♣r♦♠ s stts♥ ts ♣♥♦♠♥♦♥ ♥ss s②♠♣t♦t②

♦s② ♥ t ♥♠r ♦ sss s ① ♥ t s③ ♦ s♠♣ s s♠t st♥ ♣r♦♠ ♥ ♦r ♥ s s ♦r s ♥tr② t♦ st ♣r♦rs t ♥ ❬t❪

♠r ①♣r♠♥ts ♦♥ s♠t t sts

♥ ♦rr t♦ strt t ♣r♦♣rts ♦ t ♠♦ t♦ ♥♠r ①♣r♠♥tsr ♣r♦r♠ rst ♦♥ ♦♥ssts ♥ s♠t♥ t ♦r♥ t♦ t ♣r♦♣♦s♠♦ ♥ t♦ st② t ♦♥r♥ ♦ t st♠ts s♦♥ ♦♥ ♦♥ssts ♥s♠t♥ t ♦r♥ t♦ ♠①tr ♦ P♦ss♦♥ strt♦♥s ❬❪ ♥ ♦rr t♦s♦ t r♦st♥ss ♦ t ♣r♦♣♦s ♠♦ st♠t s ♦♠♣t ② r♥t ♣r♠trs s♠♣ ② t s ♦rt♠

①♣r♠♥t ♦♥t♦♥s

♦r stt♦♥ s♠♣s r ♥rt ♥ t ♦rt♠ s ♥t③t t ♠①♠♠ ♦♦ st♠t ♦ t ♦♥t♦♥ ♥♣♥♥ ♠♦ r♥♥ s ♣r♦r♠ r♥ trt♦♥s ♥ t ♣r♠tr ♥t③t♦♥ sr♥t ♥ t ♥trss ♣♥♥s r s♠ ♦rt♠ s st♦♣♣ tr

Page 189: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t

trt♦♥s ♠①♠♠ ♣♦str♦r st♠t s ♣♣r♦①♠t ② t ♠♥♦ t s♠♣ ♣r♠trs r r♥ s ♣♣r♦①♠t trt♦♥s ♦ ♦♥tr♦ ♠t♦

♠t♦♥ ① rs ♦♥ ♦♥t♥♦s ♦♥ ♥tr ♥ ♦♥ ♥r②❲ ♦♥sr t ♠①tr ♠♦ ♦ ss♥ ♦♣s t ♥ ①♠♣ ♥♦♠♣♦s t ♦♥ ♦♥t♥♦s r ♦♥ ♥tr r ♥ ♦♥ ♥r② rr strts t rs♥ ♦r ♦ t r r♥ ♦t ♠♦ t t ♠①♠♠ ♣♦str♦r st♠t r♦♠ t ♠♦ t t tr ♣r♠trs ♦r♥ t♦ t s♠♣ s③ ♥ t ♠① s s s♠t♦♥ strtst ♦♦ ♦r ♦ t tr♦♣♦st♥s ♦rt♠ rtr♠♦r t ♣♣r♦①♠t♦♥ ♦ t ♠①♠♠ ♣♦str♦r st♠t ② t ♠♥ ♦ t ♣r♠trss♠♣ ② ts ♦rt♠ s ♥t

100 200 400 800 1600

0.00

0.05

0.10

0.15

0.20

size of sample

Kul

lbac

k−Le

ible

r di

verg

ence

r rs ♦ t r r♥ ♦ t ♠♦ t t♠①♠♠ ♣♦str♦r st♠t r♦♠ t ♠♦ t t tr ♣r♠tr

♠t♦♥ ♦st♥ss ♦ t ♠①tr ♠♦ ♦ ss♥ ♦♣s r♥ts ①♣r♠♥ts t r s♠♣ ♦r♥ t♦ rt P♦ss♦♥ ♠①tr ♠♦❬❪ ♦s t ♠r♥ ♣r♠trs r ♥♦t ② αk = (λk1, λk2, λk3) s♠t♦♥ s ♣r♦r♠ t t ♦♦♥ s ♦ t ♣r♠trs

π = (1/3, 2/3), λ1h = h ♥ λ2h = 3 + h, ♦r h = 1, 2, 3.

rr♦r rt ♦ ts ♠♦ ♦♠♣t t t ②s r s q t♦ 9.5% stss♦ tt t ①t② ♦ t ♠①tr ♠♦ ♦ ss♥ ♦♣s ♦s t♦ ♥t②t ts s♠t t ♥ t r r♥ ♦♠s r② s♠♥ t s③ ♦ t s♠♣ ♥rss rtr♠♦r t rr♦r rt ♦ t ♠♦ s♠st♦ ♦♥r t♦ st tt t rr t♥ t t♦rt ♦♥ 9.5% ❲ s♦♥♦t tt t ♠r♥ ♣r♠trs ♦ ♦t ♦♠♣♦♥♥ts ♥ t ♦rrt♦♥ ♦♥tss♠ t♦ ♦♥r t♦ tr tr s

♥②ss ♦ tr r t sts

❲ ♥♦ str tr r t sts ② s♥ t ♠①tr ♠♦ ♦ ss♥♦♣s ♣r♠trs r st♠t t tr♦♣♦sts ♦rt♠♥t③ ♦♥ t ♠①♠♠ ♦♦ st♠t ♦ t ♦♥t♦♥ ♥♣♥♥

Page 190: Model-based clustering for categorical and mixed data sets

♥②ss ♦ tr r t sts

50 100 200 400 800

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

size of sample

Kul

lbac

k−Le

ible

r di

verg

ence

50 100 200 400 800

0.10

0.15

0.20

0.25

0.30

size of sample

Bay

es' e

rror

50 100 200 400 800

24

68

10

size of sample

β 11

50 100 200 400 800

0.0

0.2

0.4

0.6

0.8

size of sample

Γ 1[1

,2]

r sts ♦ ♠t♦♥ r r♥ ♦ t st♠t ♠♦ r♦♠ t tr ♦♥ rr♦r rt ♦ t st♠t ♠♦ ❱♦ t rst ♠r♥ ♣r♠tr ♦r t ss ❱ ♦ t ♦rrt♦♥ ♦♥tt♥ ♦t rs ♦r ss

♠♦ r♥♥ s ♣r♦r♠ r♥ trt♦♥s ♥ t ♣r♠tr ♥t③t♦♥ s r♥t ♥ t ♥trss ♣♥♥s r s♠ ♦rt♠ sst♦♣♣ tr trt♦♥s ♥ t st♠t s ♦t♥ ② t♥ t ♠♥ ♦ ts♠♣ ♣r♠trs ♠♦ st♦♥ s ♣r♦r♠ ② s♥ t♦ ♥♦r♠t♦♥rtr rtr♦♥ ❬❪ rtr♦♥ ❬❪ ♦♠♣t ♦♥ t ♠①♠♠ ♣♦str♦r st♠t

r s♦rr t st

t

s t st ❬♦r❪ srs ♥s ② ♦♦ tsts rt♦t t♦ s♥st t♦ r s♦rrs tt ♠t rs r♦♠ ①ss ♦♦♦♥s♠♣t♦♥ ♦♥t♥♦s rs ♥ ② t ♥♠r ♦ qrt♣♥t q♥ts♦ ♦♦ rs r♥ ♣r ② ♦♥ ♥tr r

♦ st♦♥

❲ st♠t t tr ♠①tr ♠♦s ♦♥t♦♥ ♥♣♥♥ ♦♥ tr♦sst ss♥ ♦♣ ♠①tr ♥ ♦♠♦sst ss♥ ♦♣ ♠①tr ♦r r♥t ♥♠rs ♦ sss ♣rs♥ts t s ♦ ♦t s ♥♦r♠t♦♥

Page 191: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t

rtr s ♦ ♦t rtr ♦t♥ t t ♦♠♣♦♥♥t ♦♠♦sst♠①tr ♠♦ ♦ ss♥ ♦♣s r t st ♦♥s ♦r ♥♦t tt t tr♠♦s st t♦ ♦♠♣♦♥♥ts

♦♥ ♥♣t

tr♦ ♦♠♦

♦♥ ♥♣t tr♦ ♦♠♦

❱s ♦ t ♥ rtr ♦r t tr ♠①tr ♠♦s st♠t♦♥ t r s♦rr t st

♥tr♣rtt♦♥ ♦ t st ♠♦

❲ ♥♦ sr t st ♠♦ ♦r♥ t♦ ♦t rtr t ♦♠♦sst♦♠♣♦♥♥t ♠①tr ♠♦ ♦ ss♥ ♦♣s ② s♥ t ♠r♥ ♣r♠trs♥ t ♥trss ♣♥♥s s♠♠r③ ② r ♠♦ ♦♥srst♦ sss ♦s t ♠♦rt② ♦♥ π1 = 0.60 r♦♣s t ♥s ♥ str♦♥ ♦♦ ♦♥s♠♣t♦♥ β1r♥s = 10.6 ♥ r s ♦ t ♦♦tsts s♣② ♦r t tsts ♦t ♥ ♠♠t ♠♥♦rt② ss r♦♣s t♥s ♥ s♠ ♦♦ ♦♥s♠♣t♦♥ β2r♥s = 1.36 ♥ s♠r s♦ t ♦♦ tsts ♦r ♦t sss t tr ♦♦♥ ♦♦ tsts r ♣♦st②♦rrt t ♣t ♦♣t ♥ ♠♠t t tst s ♣♦st② ♦rrtt t ♥♠r ♦ ♦♦ r♥s

Gammagt

0 50 100 150 200 250 3000.00

00.

005

0.01

00.

015

0 2 4 6 8 11 14 17 20 23 26 29 32 35 38

Drinks

0.00

0.04

0.08

0.12

πkp(xj |βkj , z = k)

−1.0 −0.5 0.0 0.5 1.0

−1.

0−

0.5

0.0

0.5

1.0

inertia: 36 %

iner

tia: 2

1 %

Mcv

Alkphos

Sgpt

SgotGammagt

Drinks

❱rs ♥ t rst ♦rrt♦♥ r ♥ ② Γk

r ♠♠r② ♦ t ♦♠♦sst ♦♠♣♦♥♥t ♠①tr ♠♦ ♦ ss♥ ♦♣s ♦r t r s♦rr t st ss s s♣② ♥ ♥ ss ♥ r

Page 192: Model-based clustering for categorical and mixed data sets

♥②ss ♦ tr r t sts

Prtt♦♥ st②

s t rs r ♥♠r r ♥ s♣② t ♥s ♥tr ss ♠♠rs♣s ♥ t rst ss ♣ ♠♣ ♦r s sss r ♥♦t s♣rt ♥ ts ♠♣ t strtr ♦ t t s ♥♦t r♦t ♦t sr s♣②s t ♥s ♥ t rst ♣ ♠♣ ♦ ss ♥ ts ♠♣sss r ttr s♣rt s♥ t rst ss rs s ♥tr ts♦♥ ss r tr♥s s ♦♥ t t♦♣ ♣rt ♦ t r♣ ♦ t s♦♥ ①ss sr♠♥♥t s s♠♠r② s ♥ r♠♥t t t ss ♥tr♣rtt♦♥ s♥ts ①s s t ② t rs ♥ r♥s r t♠ss sr♠♥♥t♦r♥ t♦ tr ♠r♥ ♣r♠trs

−2 0 2 4 6

−4

−3

−2

−1

01

23

First component (inertia 42%)

Sec

ond

com

pone

nt (

iner

tia 1

8%)

rst ♠♣ ♦ P

−15 −10 −5 0

−6

−4

−2

02

46

inertia 36 %

iner

tia 2

1 %

rst ♠♣ ♦ t P ♦ t ss

r ❱s③t♦♥ ♦ t ♣rtt♦♥ ② t ♦♠♦sst ♦♠♣♦♥♥t ♠①tr ♠♦ ♦ ss♥ ♦♣s ♦r t r s♦rr t st ss s r♥ ② rs ♥ ss ② r tr♥s

♦t tt t ♣rtt♦♥s ♦t♥ ② t tr ♦♠♣♦♥♥t ♠♦s r s♠rt ♥♦t ♥t s s♦♥ ②

tr♦

♦♠♦ ♦♠♦

♦♥ ♥♣t

♦♠♦ ♦♠♦

♦♥s♦♥ ♠trs t♥ t ♣rtt♦♥ ♦t♥ ② t ♦♠♦sst ♦♠♣♦♥♥t ♠♦ ♥ t ♣rtt♦♥ ♦t♥ ② t tr♦sst ♦♠♣♦♥♥t ♠♦ t ♦♥t♦♥ ♥♣♥♥ ♠♦

♦♥s♦♥

♥ ts t st t ♠①tr ♠♦ ♦ ss♥ ♦♣s ttr ts t t♦r♥ t♦ t ♥♦r♠t♦♥ rtr t♥ t ♦♥t♦♥ ♥♣♥♥ ♠♦ ♥

Page 193: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t

♦t ♠♦s st t s♠ ♥♠r ♦ sss ♣ ♣r ss ♦s t♦s♠♠r③ t ♥trss ♣♥♥s ♥ t♦ r♥ ♦t t s♣rt♦♥ ♦ ♦tsss ♥ ② ss ♣

❲♥ t st

t

t st ❬+❪ ♦♥t♥s r♥ts ♦ t P♦rts ❱♥♦ ❱r♥ r ♥s ♥ t ♥s sr ② ♥ ♣②s♦♠♦♥t♥♦s rs ① t② ♦t t② tr t② rs sr♦rs r sr ♦① t♦t ♥st② ♦① ♥st② ♣ s♣ts ♦♦♥ ♦♥ ♥tr r qt② ♦ t ♥ t ② ①♣rts ♥s ♦t ♥s r ♦r t r ♥ ♥ str t t st t tr r♥t♠①tr ♠♦s ♦t tt ♦♥ t ♥ ♥♠r s ① ♦ t st②s♥ t s ♥ ♦tr

♦ st♦♥

❲ st♠t t tr ♠①tr ♠♦s ♦♥t♦♥ ♥♣♥♥ ♦♥ tr♦sst ss♥ ♦♣ ♠①tr ♥ ♦♠♦sst ss♥ ♦♣ ♠①tr ♦r r♥t ♥♠rs ♦ sss ♥ ♣rs♥t t s ♦ ♦t s ♥♦r♠t♦♥ rtr ♥ ♦t rtr st♥t② st t ♦♠♣♦♥♥t tr♦sst ♠①tr♠♦ ♦ ss♥ ♦♣s ❲ ♥♦ s♦ tt ts ♠♦ ♦s t♦ s♣rtt t ♥s r♦♠ t r ♦♥s t♥ t ♠♦ ♥tr♣rtt♦♥

♦♥ ♥♣t

tr♦ ♦♠♦

♦♥ ♥♣t tr♦ ♦♠♦

❱s ♦ t ♥ rtr ♦r t tr ♠①tr ♠♦s st♠t♦♥ t ♥ t st

Prtt♦♥ st②

♣rs♥ts t ♦♥s♦♥ ♠trs ♥ ♦rr t♦ ♦♠♣r t r♥ ♦ tst♠t ♣rtt♦♥s ♦r♥ t♦ t tr ♦♥ ♥ ♦♦r s rsts str♥t♥t tt t ♠♦ st tt♥ t t s t ♦♠♣♦♥♥t tr♦sstss♥ ♦♣ ♠①tr ♠♦s ♥ ts ♣rtt♦♥ s t ♦sst t♦ t tr ♦♥

r s♣②s t ♥s ♥ ♣ ♠♣ ♦ ♦t sss st♠t ②t ♦♠♣♦♥♥t tr♦sst ♠①tr ♠♦ ♦ ss♥ ♦♣s ♦r♥ t♦ts sttr♣♦ts sss r s♣rt ❲ ♥♦ t ts ♣r♠trs

Page 194: Model-based clustering for categorical and mixed data sets

♥②ss ♦ tr r t sts

t r ♥

t r ♥

t r ♥

❱s ♦ t st ♥ ♥① ♥ ♦♥s♦♥ ♠trs t♥ ttr ♣rtt♦♥ ♥ t st♠t ♣rtt♦♥ ② t ♦♠♣♦♥♥t tr♦sstss♥ ♦♣ ♠①tr t tr♦♠♣♦♥♥t ♦♠♦sst ss♥ ♦♣♠①tr t ♦r♦♠♣♦♥♥t ♦♥t♦♥ ♥♣♥♥ ♠①tr

−5 0 5

−15

−10

−5

05

inertia 9.7 %

iner

tia 7

.8 %

P ♦ t ss ♠♣

−5 0 5 10

−10

−5

05

inertia 22.5 %

iner

tia 1

8.8

%

P ♦ t ss ♠♣

r ❱s③t♦♥ ♦ t ♣rtt♦♥ ② t tr♦sst ♦♠♣♦♥♥t ♠①tr ♠♦ ♦ ss♥ ♦♣s ♦r t ♥ t st ss s r♥ ② rs ♥ ss ② r tr♥s

♥tr♣rtt♦♥ ♦ t st ♠♦

♦♦♥ ♥tr♣rtt♦♥ s s ♦♥ t ♠r♥ ♣r♠trs ♦ t ♦♠♣♦♥♥ts ♥ ♦♥ t ♥trss ♦rrt♦♥ ♠trs s♠♠r③ ② r ♠♦rt② ss π1 = 0.59 s ♣r♥♣② ♦♠♣♦s t t ♥s s ss srtr③ ② ♦r rts ♦ t② ♣ ♦rs ♥ s♣ts t♥ t♠ ♦ t♠♥♦rt② ss π2 = 0.41 s ♣r♥♣② ♦♠♣♦s ② r ♥s ♠♦rt②ss s rr s ♦r ♦t sr ♦① ♠srs ♥ t ♦♦ rt ♦ttt t ♥ qt② ♦ ♦t sss s s♠r β1qt② = 5.96 ♥ β2qt② = 5.58 ♠♦rt② ss s rtr③ ② str♦♥ ♦rrt♦♥ t♥ ♦t sr ♠srs ♦♣♣♦st t♦ str♦♥ ♦rrt♦♥ t♥ t ♥st② ♥ t② ♠srs ♠♥♦rt② ss ♥r♥s tt t ♥ qt② s ♣♥♥t t rr ♦♦rt ♥ s♠ s ♦r t ♦rs ♥ t② ♠srs

♦♥s♦♥

♥ ts t st t ss♥ ♦♣ ♠①tr ♠♦s ♦s t♦ r t ♥♠r ♦ sss ♥ t♦ ttr t t t rtr♠♦r ts ♠♣t ♦♥ t st♠t

Page 195: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t

volatile.acidity

0.5 1.0 1.5

01

23

residual.sugar

0 10 20 30 40 50 60

0.00

0.04

0.08

0.12

πkp(xj |βkj , z = k)

−1.0 −0.5 0.0 0.5 1.0

−1.

0−

0.5

0.0

0.5

1.0

inertia: 31.9 %

iner

tia: 1

3 %

fxd.

vlt.

ctr.

rsd.

chlrfr..tt..

dnst

pH

slph

alchqlty

❱rs ♥ t rst ♦rrt♦♥r ♥ ② Γ2

r ♠♠r② ♦ t tr♦sst ♦♠♣♦♥♥t ss♥ ♦♣ ♠①tr♠♦ ♦r t ♥ t st ss s r♥ ♥ ♥ ss ♥ r

♣rtt♦♥ s s♥♥t s ♦♥ t ♥ sttr♣♦ts ♥ t ♠♦ ♣ tst♠t sss r r♥t s♥ t② r s♣rt ♥② t st♠t♦♥♦ t ♥trss ♣♥♥s ♣s t ♥tr♣rtt♦♥ s♥ ts ♥r♥s t ♥t♥ t ♥ qt② ♦ t ♠♥♦rt② ss ♥ ts ♣②s♦♠ ♣r♦♣rts

♦rst r t st

t

s t st srs ♦rst rs ❬❪ ♥ t ♥♦rtst r♦♥ ♦ P♦rt② s♥ ♠t♦r♦♦ rs s♥ ♦♥t♥♦s rs ♦r ♦t t ❲s②st♠ ♥ t♦ ♦t t ♠t♦r♦♦② t♠♣rtr ♥rt ♠t② t♦ ♥tr rs rt t♦ t s♣t ♦♦r♥ts ♥ tr♥r② ♦♥s ♥t♥ t ♣rs♥ ♦ r♥ t ss♦♥ s♠♠r ♦r ♥♦t s♠♠r ♥t ② ♥ ♦r ♥♦t ♥

♦ st♦♥

♣rs♥ts t s ♦ ♦t s ♥♦r♠t♦♥ rtr ♦r t tr♠①tr ♠♦s ♦r♥ t♦ ♦t rtr t ♠♦ ttr tt♥ t t s t♦♠♦sst ♠①tr ♠♦ ♦ ss♥ ♦♣s t tr ♦♠♣♦♥♥ts

♥tr♣rtt♦♥ ♦ t st ♠♦

♦♦♥ ♥tr♣rtt♦♥ s s ♦♥ t ♠r♥ ♣r♠trs ♦♥ t ♥trss ♦rrt♦♥ ♠trs s♠♠r③ ♥ r ♠♦rt② ss π1 = 0.57r♦♣s t rs ♦♣ t t♠♣rtr ♥ s♠ rt ♠t② ♠srs ♦ ♥ r s♦♥ ss π2 = 0.26 r♦♣st ♥tr rs s rs r ♦♣ t str♦♥ ♥ ♥ ♥♦ r♥

Page 196: Model-based clustering for categorical and mixed data sets

♥②ss ♦ tr r t sts

♦♥ ♥♣t

tr♦ ♦♠♦

♦♥ ♥♣t tr♦ ♦♠♦

❱s ♦ t ♥ rtr ♦r t tr ♠①tr ♠♦s st♠t♦♥ t ♦rst r t st

t ❲ ♠srs t s♠ s ♠♥♦rt② ss π3 = 0.17) r♦♣s ts♠♠r rs ♦♣ t s ♦ ❲ ♠srs ①♣t t ♦♥ t♠♣rtr s ♠♥ t t rt ♠t② s ♥trss ♦rrt♦♥♠tr① ♥r♥s t ♣♥♥s t♥ t s♠♠r ♥ t♠♣rtr ♥s ♦ ♥ ♥② ♥♦t tt t s♣ ♦♦r♥ts r♦② ♦♦t s♠ strt♦♥ ♥ t tr sss

temp

5 10 15 20 25 30

0.00

0.02

0.04

0.06

no yes0.0

0.2

0.4

summer

C 1 C 2 C 3 C 1 C 2 C 3

πkp(xj |βkj , z = k)

−1.0 −0.5 0.0 0.5 1.0

−1.

0−

0.5

0.0

0.5

1.0

inertia: 23 %

iner

tia: 1

8.4

%

Xaxs

Yaxs

smmr

wknd

FFMCDMCDC

ISI

temp

RH

wind

rain

❱rs ♥ t rst ♦rrt♦♥r ♥ ② Γk

r ♠♠r② ♦ t ♦♠♦sst ♦♠♣♦♥♥t ♠①tr ♠♦ ♦ ss♥ ♦♣s ♦r t ♦rst r t st ss s s♣② ♥ r♥ ss ♥ r♥ ss ♥

Prtt♦♥ st②

♦t tt t ♣rtt♦♥s ♦t♥ ② t tr ♠♦s r s♠r t ♥♦t ♥ts s♦♥ ②

Page 197: Model-based clustering for categorical and mixed data sets

♣tr ♦s str♥ ♦ ss♥ ♦♣s ♦r ♠① t

tr♦

♦♠♦ ♦♠♦ ♦♠♦

♦♥ ♥♣t

♦♠♦ ♦♠♦ ♦♠♦

♦♥s♦♥ ♠trs t♥ t ♣rtt♦♥ ♦t♥ ② t ♦♠♦ssttr♦♠♣♦♥♥t ♠♦ ♥ t ♣rtt♦♥ ♦t♥ ② t tr♦sst ♦♠♣♦♥♥t ♠♦ t ♦♥t♦♥ ♥♣♥♥ ♠♦

♦♥s♦♥

♠♦ ♣♦♥ts ♦t tr sss ♦ ♦rst rs t s ♠♦r ♣rs t♥ t♦♥t♦♥ ♥♣♥♥ ♠♦ r♦② s♣rts t s♠♠r rs r♦♠ t♦tr ♦♥s ♥ t ♦♠♦sst ♠①tr ♠♦ ♦ ss♥ ♦♣s ♦♥srst♦ ♥s ♦ s♠♠r rs rstrt♦♥s ♦♥ ♦♥ t ♣r♠trs s♣s ♦ t♦ttr t t t t♥ t tr♦sst ss♥ ♦♣ ♠①tr ♠♦ ♦r♥ t♦ ♦t rtr ts ♠♣t s s♥♥t s♥ t ♥♠rs ♦ sss st② ♦t ♠♦s r r♥t

♦♥s♦♥

♠①tr ♠♦ ♦ ss♥ ♦♣s ss t ♣r♦♣rts ♦ ♦♣s ♥♣♥♥t ♦ ♦ t ♠r♥ strt♦♥s ♥ ♦ t ♣♥♥② rt♦♥s s ts♠①tr ♦s t♦ ① ss strt♦♥s ♦♥♥ t♦ t ①♣♦♥♥t ♠② ♦rt ♦♥♠♥s♦♥ ♠r♥ strt♦♥s ♦ ♦♠♣♦♥♥t ♦r♦r t ts ♥t♦♦♥t t ♥trss ♣♥♥s ♥ ♣♣r♦ s ♦♥ ♣ ♣r ss ♦ tss♥ t♥t r ♦s s♦ t♦ s♠♠r③ t ♠♥ ♥trss ♣♥♥s♥ t♦ s③ t t ② s♥ t ♠♦ ♣r♠trs

r♥ ♦t ♥♠r ①♣r♠♥ts ♥ ♣♣t♦♥s ♣♦♥t ♦t tt ts♠♦ s s♥t② ① t♦ t t r♥ ② ♥ ♦tr ♦♥ rtr♠♦r t ♥r t ss ♦ t ♦♥t♦♥ ♥♣♥♥ ♠♦ ♦r ♥st♥ t rt♦♥♦ t ♥♠r ♦ sss

♥♠r ♦ ♣r♠trs ♥rss t t ♥♠rs ♦ sss ♥ rss♣② s ♦ t ♦rrt♦♥ ♠trs ♦ t ss♥ ♦♣s ♦ ♦ tsr ♣r♦♣♦s ♦♠♦sst rs♦♥ ♦ t ♠♦ ss♠♥ t qt②t♥ t ♦rrt♦♥ ♠trs s ♠♦ ♠② ttr t t t t♥ ttr♦sst ss♥ ♦♣ ♠①tr ♠♦ ♦r t ♥ r ♥ t♥♠r ♦ rs ♥rss ♦ ♠♦r ♣rs♠♦♥♦s ♦rrt♦♥ ♠trs ♦ ♣r♦♣♦s t♦ ♦ ts r ♥ tr ♦rs

♥② t ♠♦ ♥ ♥♦t str ♥♦♥♦r♥ t♦r rs ♥ ♠♦rt♥ t♦ ♠♦ts ♥ ♥ s s t ♠t strt♦♥ ♥t♦♥ s♥♦t ♥ ♥ rt ♦rr t♥ t ♠♦ts ♦ t♦ ♥ ♠t strt♦♥ ♥t♦♥ t ts ♠t♦ s tr ♣♦t♥t ts♦r tt♥t♦♥ s t♦ ♣ t ss♠s rr ♣♥♥s t♥ t

Page 198: Model-based clustering for categorical and mixed data sets

♦♥s♦♥

♠♦ts ♦ t♦ rs ts st♠t♦♥ ♦ s♦ ♦♥ t st♠t♦♥ ♦rt♠♥ ts stt② ♦ t♦ st

Page 199: Model-based clustering for categorical and mixed data sets
Page 200: Model-based clustering for categorical and mixed data sets

♦♥s♦♥ ♦ Prt

❲ s♥ tt t s ♠♣♦rt♥t t♦ ♣r♦r♠ t str ♥②ss ♥ t ♥t s♣ ♦ t rs ♥ ♦rr t♦ ♣r♦ ♠♥♥ rsts ♥ t♠t♦s ♦♥ ♠①tr ♠♦s s r♥t t srs r♦♠ ♦ ♠trtstrt♦♥s ♦r ♠① t

ss♠♣t♦♥ ♦ t ♦♥t♦♥ ♥♣♥♥ t♥ t rs s ♠♥♥ ♠♦ s♥ t ♣r♦s ss strt♦♥s ♦r t ♦♥♠♥s♦♥♠r♥s ♦ t ♦♠♣♦♥♥ts s ♠♦ s r♥t s♣② ♥ t s♠♣ s③ ss♠ ♦r♥ t♦ t ♥♠r ♦ rs ♥ ♥ s s t ♥♦r♠t♦♥♦♥ t ♥trss ♣♥♥② s ♥♦t ♣rs♥t ♥ t t st ♦r t ♥ ♥ssr② t♦ r① t ♦♥t♦♥ ♥♣♥♥ ss♠♣t♦♥

♠①tr ♦ ♦t♦♥ ♠♦s ♥ ts ①t♥s♦♥ ♣r ♦s s s♦ ♥ tr♥t t♦ t ♦♥t♦♥ ♥♣♥♥t ♠♦ ♦t tt ts ①t♥s♦♥ ♣r ♦s♣♣rs t♦ ♠♦r ♥t s♥ t ♥♠r ♦ ♣r♠trs st②s ♠t ♦rt ♠♦ ♥tr♣rtt♦♥ ♥ t t♦ ♣r♦r♠ ② t ♣rtt♦♥r s♥ t♦♥♠♥s♦♥ ♠r♥ strt♦♥s ♦ t ♦♠♣♦♥♥ts r ♥♦t ss ♦r t♦♥t♥♦s rs

s♦♥ tr♥t ♦♥ssts ♥ t ♥r♥ ss♥ ♠①tr ♠♦ ♣♣rs s ♠♦r ♠♥♥ ♦r ts ♠t♦ s ♦r t ♣r♠tr st♠t♦♥r♠t② ♠ts t ♥♠r ♦ srt rs

♥ ts ♦♥t①t t♦ ♠♥ ♦ts ♣♣r t♦ s s r t ♠♦ ♠stt♦ ♣r♦ ss ♦♥♠♥s♦♥ ♠r♥ strt♦♥s ♦r ts ♦♠♣♦♥♥ts ♥ t♠st ♣r♦ ♠♥♥ ♦♥ts rt♥ t ♥trss ♣♥♥s s♦♥ ♦t ♦ts ♣r♦♣♦s t♦ ♠①tr ♠♦s

rst ♠♦ ♦s t♦ ♣r♦r♠ t str ♥②ss ♦ t sts t ♦♥t♥♦s♥ t♦r rs t rs r♦♠ t ♠t t♥t ss ♠♦ ♦♣ ♦r ♥trss ♣♥♥t t♦r rs ♥ t ♦♠♣♦♥♥ts ♦ ts♠♦ r ♦♠♣♦s t ss♥ ♥ ② ♦st strt♦♥s s♣t②♦ ♦r ♣♣r♦ s t♦ s♠t♥♦s② ♣r♦r♠ t ♠♦ st♦♥ ♥ t ♣r♠trst♠t♦♥ ♥ ♠ ♦rt♠

s♦♥ ♠♦ s ♠①tr ♦ ss♥ ♦♣s s ♠♦ s r② ♥rs♥ t ♣r♦r♠s t str ♥②ss ♦ ♠① t sts t rs ♠tt♥ ♠t strt♦♥ ♥t♦♥s ♦r♦r t ♣r♦s s♦♠ s③t♦♥ t♦♦s t♦s♠♠r③ t ♥trss ♣♥♥s ♥ t♦ s♣② t ♥s ♦r t♠♦ ♦♠♣①t② ♥rss t t ♥♠r ♦ rs ♥ ♦r t ♦♠♦sstrs♦♥ s ts ♠♦ ♣♣rs s ♥♣♣r♦♣rt ♦r t sts t r ♥♠r♦ rs ♦ ♠♦r ♣rs♠♦♥♦s rs♦♥s ♦ ♦♥sr t♦ str s tsts

Page 201: Model-based clustering for categorical and mixed data sets
Page 202: Model-based clustering for categorical and mixed data sets

♥r ♦♥s♦♥ ♥ ♣rs♣ts

♦♥s♦♥

♥ ts tss ♥ ♥trst ♥ t str ♥②ss ♦ ♦♠♣① t♦r ♣rs② ♦s ♦♥ t t♦r ♥ ♠① t sts ♦t s t♦ ♥tr♦ ♠♦s ♣♣r♦s ♥ ♦rr t♦ str s t ②♠♦③♥ t ♥trss ♣♥♥s ♦r♦r ts ♠♦s t♦ s♠♠r③t t strt♦♥ ② ♣r♠trs t♦ tt t ♥tr♣rtt♦♥

♦ ♠♦s ♥ ♣rs♥t t♦ ♣r♦r♠ t str ♥②ss ♦ t♦rt sts ♠♥ s t♦ r♦♣ t rs ♥t♦ ♦♥t♦♥② ♥♣♥♥t♦s ♥ t♦ ♣t ♣rs♠♦♥♦s strt♦♥ ♦r ♦ ♦♠♥t♦r♣r♦♠s r qt♦s ♥ t t♦r t sts t ♥trss ♣♥♥sr ♥②③ ♦ t ♣rs♥t ♠♦s sr r♦♠ ts ♣r♦♠ r♥ t ♠♦st♦♥ st♣ ♥ t♦ ②s♥ ♣♣r♦s r ts r t s ♥♦trst t♦ ♣r♦r♠ t ts ♠♦s t str ♥②ss ♦ t st t ♦t♦ rs ♦r ♥ t rs r ♦r♥ ♦r ♥r② ♣♦ss ♥srt♦ ts ♣r♦♠ ♥ ♥ ② t ♠♦s ♦♣s

♠①tr ♠♦ ♦ ss♥ ♦♣s s ♥ ♥tr♦ t♦ str ♠①t sts s ♠♦ ♣r♠ts t♦ ♦t♥ ss ♦♥♠♥s♦♥ ♠r♥s ♦r ♦♠♣♦♥♥ts ♥ t♦ ♠♦③ t ♥trss ♣♥♥s ♥r ♠①tr♠♦ ♦ ss♥ ♦♣s ♦s ♥♦t sr r♦♠ ♦♠♥t♦r ♣r♦♠s t♦ ♣r♦r♠t ♠♦ st♦♥ s ts ♠♦ ♥ ♥ ♥t tr♥t t♦ t ♠♦s♣ t♦ t t♦r t ♥ t rs r ♥r② ♦r ♦r♥ ♥ t♦s t ♦♠♥t♦r ♣r♦♠s ♦ t ♠♦ st♦♥

Prs♣ts

r♦♦t ts tss ♥ ss ② t st ♦ t ♥sr♥ ② t s♠ strt♦♥ ♦r tr♥t ♥t♦♥s ♦ ss ♦ s ❬+ ♥❪

♠♦s ♥ ♥tr♦ ♥ str♥ r♠♦r ♦s② t②♥ s ♥ s♠s♣rs ♦r ♥ sst♦♥ ♦♥t①t ♦r ♦♥ ♥①♣t tt ts ♠♦s ♦t♣r♦r♠ t sr♠♥♥t ♣♣r♦s t ♦strrss♦♥ ♦♥② ♥ ♥s r ♥ tr ♦t s ♠♦r

Page 203: Model-based clustering for categorical and mixed data sets

♣tr

♠t♦s t♥ t sr♠♥♥t ♣♣r♦s s♥ t② ♠♦③ t t strt♦♥ t sr♠♥♥t ♣♣r♦s ♦s ♦♥ t ♦♥rs t♥ sss

♠♦s ♥tr♦ ♥ ts tss ♦ ♠♥ t sts t ♠ss♥ s♥ tr st♠t♦♥ ♥ ♣r♦r♠ ② ♥ ♦rt♠ ♦r ② s s♠♣r r ♥♦♥ t♦ ♠♥ s t

♠♦s st♠t ♦r♥ ♠tr① ♥ rqr r ♥♠r ♦♣r♠trs ♦ t s ♠♣♦rt♥t t♦ ♥tr♦ s♦♠ ♣rs♠♦♥♦s rs♦♥s ♦ t♠①tr ♠♦ ♦ ss♥ ♦♣s ♥ ♦rr t♦ ♠♥ t sts t r ♥♠r♦ rs s ♦♥ t ♦♠tr ♣♣r♦s s ♦r t ss♥ ♠①tr♠♦s ❬❪ s♦♠ ♦♥str♥s ♦ ♦♥ t ♦rrt♦♥ ♠tr① ♦ tss♥ ♦♣s ♦r ts ♣♣r♦ ♦ ♠ t st♠t♦♥ rr tr♣rs♠♦♥♦s ♣♣r♦s ♥rt r♦♠ t ss♥ r♠♦r ♦ s♦ s♦r ♥st♥ t ♠♦s ♦r r t sts ❬❪ ♥② ♥♦tr rsr ①s♦ ♦♥sst ♥ ♥r③t♦♥ ♦ t ♠①tr ♠♦ ♦ ♣♥♥② trs ♥t ♦♣s ♥ t rt strt♦♥ ♦r ♥② ♦♣s ♦ ♠① rs t② ♠t ♦r♦r t ♠♦ st♦♥ st♣ ♦ ts ♠t♦ s ss♥② ts ♣♣r♦ ♦ ♦ ♥ ♥r♥ ② ♠①♠③t♦♥ ♦ t ♦♦② s♥ ♠t♦s ♥rt r♦♠ ❬❪ s ♥♦ ♣r♦r ♥♦r♠t♦♥ ♦ t ts ♠t♦ ♦ ♥r♥

♦rrt♦♥ ♦♥t ♦ t ss♥ ♦♣ s ♦♦ ♣r♦♣rts ♥ t♠r♥ strt♦♥s r st♠t ♦r st t ♠r♥ strt♦♥s ♦ t ♦♠♣♦♥♥ts ♦r t ♠①tr ♠♦ ♦ ss♥ ♦♣s s s♠♣r♠tr ♣♣r♦ ♦r ♥st♥ s ♦♥ t ♦rs ♦ ❬♦ ❲❪s♥ t ♣r♦♣rts ♦ t ♦rrt♦♥ ♦♥t ♦ s②♠♣t♦t② r♥t

♥② t ♠①tr ♦ t t♦ ①tr♠ ♣♥♥② strt♦♥s ♦ ♥tr♥t t♦ t ss♥ ♦♣s s ♠♦ ♥ ♥ ② s♥ ♦♣s♥ t ♠①♠♠ ♣♥♥② strt♦♥ ♦ ♥ s t strt♦♥ tt♥s t rét♦♥ ♣♣r ♦♥ ② ♥ s♦♠ ♦♥str♥ts t strtr ♥ tr t ♠♦ st♦♥ ♦ s② ♣r♦r♠

t rts t♦ st ②♦ rt ②♦ ♥r ♦♦ ♠

♥ ♦ tr ♥ s♦t s ♥ ♦ ♥ts tr t♦

s s t ♥ ♦♦rs ♥

Page 204: Model-based clustering for categorical and mixed data sets

♣♣♥①

♣♣♥① ♦ Prt

♥r ♥tt② ♦ t ♠①tr ♦ t t♦

①tr♠ ♣♥♥② strt♦♥s

♦ strt♦♥ s ♥r② ♥t ♥ t ♦ ♦♥t♥s t sttr rs ♦r ♥ t ♦ ♦♥t♥s t st t♦ rs ♥ t st tr♠♦ts ♦ ♣r♦ ts ♣r♦♣♦♣rt② rst② s♦ t ♥r ♥tt② ♦t ♠♦ ♥ ♦t ♦ t ♦♦♥ s♠♣ ss t♦ rs t tr ♠♦ts♥ tr ♥r② rs ♥ ♦♥ t♦ t ♥r ♥tt② ♦ t♠♦

Pr♦♣♦st♦♥ ♦ rs t tr ♠♦ts ♠①tr ♠♦ ♦t t♦ ①tr♠ ♣♥♥② strt♦♥s s ♥r② ♥t ♥ dkb = 2m

kb1 = m

kb2 = 3

Pr♦♦ ♣♣♦s tt tr ①stsαkj = (ρkj, ξkb, τ kb, δkb) ♥ αkj = (ρkj, ξkb, τ kb, δkb)s s

∀xkbi p(x

kbi ;αkb) = p(x

kbi ; αkb).

❲ ♠♦♥strt tt ts qt② ♥♦s tt αkj = αkj ♠♦♥strt♦♥s s♣t ♥ tr ♣rts r tr♠♥ ② t tr ♣♦ssts ♦ (δkb, δkb)qt② ♦♥ rt♦♥ q ♦r ♦t ♣r♠trs ♥♦ rt♦♥ q ♦r ♦t ♣r♠trs ❲ s♦ tt ♥♦s t qt② t♥ t ♣♥♥② rt♦♥s δkb = δkb ♥ t♥ t ♦♥t♥♦s ♣r♠trs s ♥♦sαkb = αkb

• qt② ♦ t ♣♥♥② rt♦♥s δkb = δkb❲t♦t ♦ss ♦ ♥rt② ss♠ tt

∀h, h′ ∈ 1, . . . , 3, h 6= h′ : δh2hkb = 1 ♥ δh2h′

kb = 0.

♥ t rt♦♥ ♥ ② s t♦ t ♦♦♥ s②st♠ ♦ ♥♥ qt♦♥s♦r h ∈ 1, . . . , 3 ♥ h′ ∈ 1, . . . , 3 \ h

(1− ρkb)ξ1hkb ξ

2hkb + ρkbτ

hkb = (1− ρkb)ξ

1hkb ξ

2hkb + ρτhkb

(1− ρkb)ξ1hkb ξ

2h′

kb = (1− ρkb)ξ1hkb ξ

2h′

kb .

Page 205: Model-based clustering for categorical and mixed data sets

♣♣♥① ♣♣♥① ♦ Prt

❲ s t s♦♥ ♥ ♦ t ♣r♦s s②st♠ t t ♦♦♥ s ♦ t ♦♣(h, h′) ♥ s ♦t♥ tt

ξ11kbξ12kb

=ξ11kbξ12kb

♥ξ11kbξ13kb

=ξ11kbξ13kb

.

♦ ξ11kb = ξ11kbξ12kbξ12kb

= ξ11kbξ13kbξ13kb

r s ♥tr♣t ε ∈ R+ s tt ε = ξ12kb

ξ12kb=

ξ13kbξ13kb

r♠♥ tt∑3

h=1 ξ1hkb =

∑3h=1 ξ

1hkb = 1 ♦r♦r

3∑

h=1

ξ1hkb = ξ11kbε+ ξ12kbε+ ξ13kbε = ε.

♦ ε = 1 ❲ ♦♥ tt ξ1hkb = ξ1hkb s♠ rs♦♥♥ s s t♦ ♦t♥tt ξ2hkb = ξ2hkb r♦♠ ts ♦t♥ t qt② t♥ ρkb = ρkb ♥ τhkb = τhkb♥② ♦t♥ tt αkb = αkb

• ♥② ♦♥ rt♦♥ s q t♥ ♦t ♣r♠tr③t♦♥s❲t♦t ♦ss ♦ ♥rt② ss♠ tt δ121kb = δ222kb = δ323kb = 1 ♥ δh2h

kb = 0♦trs δ122kb = δ221kb = δ323kb = 1 ♥ δh2h

kb = 0r♦♠ t s②st♠ ♦ ♥♥ qt♦♥s ♥ ② ①trt t ♦♦♥ s②st♠

(1− ρkb)ξ13kbξ

21kb = (1− ρkb)ξ

13kb ξ

21kb

(1− ρkb)ξ13kbξ

22kb = (1− ρkb)ξ

13kb ξ

22kb

(1− ρkb)ξ11kbξ

21kb + ρkbτ

1kb = (1− ρkb)ξ

11kb ξ

21kb

(1− ρkb)ξ11kbξ

22kb = (1− ρkb)ξ

11kb ξ

22kb + ρkbτ

1kb.

r♦♠ t rst t♦ ♥s ♦ t ♣r♦s qt♦♥ tt ξ22kb = ξ21kbξ22kbξ21kb

♦♥sr t st t♦ ♥s r ξ22kb s r♣ ② ξ21kbξ22kbξ21kb

♥ r t st ♥ s

♠t♣ ② ξ21kbξ22kb

s

(1− ρkb)ξ11kbξ

21kb + ρkbτ

1kb = (1− ρkb)ξ

11kb ξ

21kb

(1− ρkb)ξ11kbξ

21kb = (1− ρkb)ξ

11kb ξ

21kb + ρkbτ

1kbξ21kbξ22kb.

s ρkbτ 1kb+ ρkbτ1kbξ21kbξ22kb

= 0 s rst s ♥ ♦♥trt♦♥ t t strt ♣♦stt②

♦ t tr♠s ♦ t s ♥♦t ♣♦ss t♦ rs♣t ♥ ♦♥② ♦♥ rt♦♥ sq t♥ ♦t ♣r♠tr③t♦♥s

• ♦ rt♦♥ q t♥ ♦t ♣r♠tr③t♦♥s

Page 206: Model-based clustering for categorical and mixed data sets

♥r ♥tt② ♦ t ♠①tr ♠♦ ♦ ♠t♥♦♠ strt♦♥s ♣r

♠♦s

❲t♦t ♦ss ♦ ♥rt② ♦♥sr t ♦♦♥ s②st♠

(1− ρkb)ξ11kbξ

21kb + ρkbτ

1kb = (1− ρkb)ξ

11kb ξ

21kb

(1− ρkb)ξ12kbξ

22kb + ρkbτ

2kb = (1− ρkb)ξ

12kb ξ

22kb

(1− ρkb)ξ13kbξ

23kb + ρkbτ

3kb = (1− ρkb)ξ

13kb ξ

23kb

(1− ρkb)ξ12kbξ

21kb = (1− ρkb)ξ

12kb ξ

21kb + ρkbτ

2kb

(1− ρkb)ξ13kbξ

22kb = (1− ρkb)ξ

13kb ξ

22kb + ρkbτ

3kb

(1− ρkb)ξ11kbξ

23kb = (1− ρkb)ξ

11kb ξ

23kb + ρkbτ

1kb

(1− ρkb)ξ11kbξ

22kb = (1− ρkb)ξ

11kb ξ

22kb

(1− ρkb)ξ12kbξ

23kb = (1− ρkb)ξ

12kb ξ

23kb

(1− ρkb)ξ13kbξ

21kb = (1− ρkb)ξ

13kb ξ

21kb

r♦♠ t ♥s ♥ ♦t♥ tt ξ11kbξ12kb

<ξ11kbξ12kb

r♦♠ t ♥s ♥ ♦t♥

tt ξ11kbξ12kb

>ξ11kbξ12kb

♦ t s ♥♦t ♣♦ss t♦ rs♣t ♥ ♥♦ rt♦♥ s q

t♥ ♦t ♣r♠tr③t♦♥s

Pr♦♣♦st♦♥ r ♥r② rs ♠①tr ♠♦ ♦ t t♦ ①tr♠

♣♥♥② strt♦♥s s ♥r② ♥t ♥ dkb = 3 mkb1 = m

kb2 =

mkb3 = 2

Pr♦♦ ♣♣♦s tt tr ①stαkj = (ρkj, ξkb, τ kb, δkb) ♥ αkj = (ρkj, ξkb, τ kb, δkb)s s

∀xkbi p(x

kbi ;αkb) = p(x

kbi ; αkb).

② rt♥ t s②st♠ t qt♦♥s rt t♦ ♦t♥ tt ∀j =1, . . . , 3 : ξj1kb(1 − ξj1kb) = (1 − ξj1kb)ξ

j1kb s ∀j = 1, . . . , 3 : ξj1kb = ξj1kb ❲

strt♦rr② ♦t♥ t qt② t♥ t ♦trs ♣r♠trs s♦ αkb = αkb

♦♥s♦♥ ♠①tr ♠♦ s st ② s♦♥ ♦ ♠♦ts ♥♦r rs♦ ♦t♥ t ♥r ♥tt② ♦ t ♠♦s ♥ rtt♥ ②s♦♥ ♦ ♠♦ts ♥♦r rs s ♦♥ ♦ t ♦♦♥ ♠♦s t tr ♥r②♦♥ ♥ t t♦ tr♠♦ts ♦♥

♥r ♥tt② ♦ t ♠①tr ♠♦ ♦

♠t♥♦♠ strt♦♥s ♣r ♠♦s

♥r ♥tt② ♦ t ♠♠ ♠♦ t tr ♦s t k0 =r♠♥

kℓkb ♥ t ♠tr① Mb r

Mb(k, h) = ατk0b(h)

kb .

② ♥♦t♥ ② ξb = mink

ℓkb + 1 ♥r②

r♥K Mb = min(g, ξb).

Page 207: Model-based clustering for categorical and mixed data sets

♣♣♥① ♣♣♥① ♦ Prt

♦r♦r② ♣r♠trs ♦ t ♠♠ ♠♦ t tr ♦s r ♥r②♥t ♣ t♦ s♣♣♥ ♣r♦

min(g, ξ1) + min(g, ξ2) + min(g, ξ3) ≥ 2g + 2.

♥r ♥tt② ♦ t ♠♠ ♠♦ t ♠♦r t♥ tr ♦s ♥t s♠ ② tt ❬❪ ♥r③ t rst t ♦s ② ♦sr♥ tt ♦s ♦ t♦r rs ♥ ♦♠♥ ♥t♦ tr t♦r rss ♥ ♣♣② t rs t♦r♠♦r♦r② ❲ ♦♥sr ♠♠ ♠♦ t ♦s r ≥ 3 tr ①sts tr♣rtt♦♥ ♦ t st 1, . . . , ♥t♦ tr s♦♥t ♥♦♥ ♠♣t② ssts S1 S2 ♥S3 s tt γi =

j∈Siξj t

min(g, γ1) + min(g, γ2) + min(g, γ3) ≥ 2g + 2,

t♥ t ♠♦ ♣r♠trs r ♥r② ♥t ♣ t♦ s♣♣♥

♦♠♣tt♦♥ ♦ t ♥trt ♦♠♣tt

♦♦ ♦ t ♠①tr ♠♦ ♦ ♠t♥♦♠

strt♦♥s ♣r ♠♦s

♥ ts t♦♥ ♣r♦♦ ♦ Pr♦♣♦st♦♥ s ♥ ❲ rst② ♥ ♥♣r♠tr③t♦♥ ♦ t ♦ strt♦♥ tt♥ t ♥trt ♦♠♣tt♦♦ ♦♠♣tt♦♥ ❲ s♦♥② ♥ t ♣r♦r strt♦♥ ♦ t ♥ ♦♣r♠tr③t♦♥ ♦r♥ t♦ t ♦tr ♣r♠tr③t♦♥ r② ♥r♥ trt♦♥ t♥ t ♠ ♠♦s ❲ ♦♥ ② t ♥trt ♦♠♣tt♦♦ ♦♠♣tt♦♥ s t trt rst

♣r♠tr③t♦♥ ♦ t ♦ strt♦♥

❲t♦t ♦ss ♦ ♥rt② ss♠ tt t ♠♥ts ♦ δkb r ♦rr ② rs♥ s ♦ t ♣r♦t② ♠ss ss♦t t♦ t♠ ♥ ♥tr♦ t ♥

♣r♠tr③t♦♥ ♦ akb ♥♦t εkb r εkb ∈ Ekb =[

1mb ; 1

]

×, . . . ,×[

1♠b−ℓkb

; 1]

♥ r εkbh s ♥ ②

εhkb =

aδkbhkb h = 1aδkbhkb∏h−1

h′=1(1−εh

′kb)

♦trs.

♠♠ ♦♥t♦♥ ♣r♦t② ♦ ①b s

p(xb|z, ℓkb, δkb, εkb) =ℓkb∏

h=1

(εhkb)n(h)kb (1− εhkb)

nhkb ,

Page 208: Model-based clustering for categorical and mixed data sets

♦♠♣tt♦♥ ♦ t ♥trt ♦♠♣tt ♦♦ ♦ t ♠①tr ♠♦

♦ ♠t♥♦♠ strt♦♥s ♣r ♠♦s

Pr♦♦

p(xb|z, ℓkb, δkb, εkb) = p(xb|z, ℓkb,αkb)

=♠b∏

h=1

(αhkb)♥hkb

=

ℓkj∏

h=1

(α(h)kb )

♥(h)kb

(

α(ℓkb+1)kb

)♥ℓkbkb

= (ε1kb)n(1)kb

ℓkb∏

h=2

[

(εhkb)♥(h)kb

(

h−1∏

h′=1

(1− εhkb)n(h)kb

)]

ℓkb∏

h=1

(1− εhkb)nℓkbkb

=

ℓkb∏

h=1

(εhkb)n(h)kb (1− εhkb)

nhkb .

Pr♦r strt♦♥

♠♠ ♣r♦r strt♦♥ ♦ εkb s

p(εkb|ω, δkb) =♠b

♠b − ℓkb.

Pr♦♦ ❲ r♠♥ tt akb|ω ∼ Dtℓkb+1

(

1, . . . , 1;♠b)

♥ tt

p(akb, δkb|ω) = p(α|ω) = p(εkb, δkb|ω).

♦ t ♣ ♦ t ♣r♦r strt♦♥ ♦ εkb

p(εkb|δkb,ω) =

∏ℓkbh=1(ε

hkb)

γhkb−1(1− εhkb)∑ℓkb+1

h′=h+1(γh

kb−1)

εkb∈Ekb

∏ℓkbh=1(ε

hkb)

γhkb−1(1− εhkb)∑ℓkb+1

h′=h+1(γh

′kb−1)dεkb

.

s εhkb ♦♦s tr♥t t strt♦♥ ♦♥ t ♣r♠trs s♣[

1♠b−h+1

, 1]

♥♦t ② Be(γhkb,∑ℓkb+1

h′=h+1(γh′

kb−1)+1) ♦ ssr t ♣♦stt② ♦ t ♣r♠trs♦ t tr♥t t strt♦♥s ♣t γhkb = 1 s♦

p(εkb|δkb,ω) =♠b

♠b − ℓkb.

t♦♥ t♥ ♠ ♠♦s

♠♠ t t ♠♦ t ℓ⊖kb ♠♦s ♥ t ♣r♠trs (δ⊖

kb, ε⊖kb) ♥ t t

♠♦ t ℓkb ♠♦s ♥ t ♣r♠trs (δkb, εkb) ♦t ♠♦s r ♥ s s

Page 209: Model-based clustering for categorical and mixed data sets

♣♣♥① ♣♣♥① ♦ Prt

tt ℓ⊖kb = ℓkb − 1 tt t ℓ⊖kb ♠♦s ♥ t rst ♣r♦ts t s♠♦t♦♥s ∀h ∈ δ⊖

kb, h ∈ δkb ♥ t s♠ ♣r♦t② ♠sss (ε⊖hkb = εhkb, h < ℓkb)s ♠ ♠♦s ♦♦ ts rt♦♥

p(xb|z, ℓkb, δkb, εkb)p(xb|z, ℓ⊖kb, δ

kb, ε⊖kb)

=(♠b − ℓkb + 1)♥

ℓkb−1

kb −1

(♠b − ℓkb)♥ℓkbkb

(εℓkb)♥(ℓkb)

kb (1− εℓkb)♥ℓkbkb .

Pr♦♦ ❲ strt ② t ♦♦♥ rt♦♥

p(xb|z, ℓkb,αkb)

p(xb|z, ℓ⊖kb,α⊖kb)

=(αℓkbkb )

♥(ℓkb)

kb (αℓkb+1kb )♥

ℓkbkb

(α⊖ℓkbkb )♥

ℓkb−1

kb

.

♦t tt εhkb = ε⊖hkb ♥ h = 1, . . . , ℓkb − 1 s♥ α(h)kb = α

⊖(h)kb ♥ τℓkb(h) =

τℓkb−1(h) ♥ h = 1, . . . , ℓkb − 1 ♥ ② s♥ t r♣r♠tr③t♦♥ ♥ εkbt ♣r♦♦ s ♦♠♣t

♥trt ♦♠♣tt ♦♦

♥trt ♦♠♣tt ♦♦ s ♥② ♣♣r♦①♠t ② ♥t♥t s♠ ♦r t srt ♣r♠trs ♦ t ♠♦s ♦t♦♥s ♥ ② ♣r♦r♠♥ t①t ♦♠♣tt♦♥ ♦♥ t ♦♥t♥♦s ♣r♠trs ②

p(xb|z, ℓkb) ≈(

1

mb − ℓkb

)♥ℓkbkb

ℓkb∏

h=1

Bi(

1♠b−h+1

; ♥(h)kb + 1; ♥hkb + 1

)

♠b − h,

r Bi(x; a, b) = B(1; a, b)−B(x; a, b) B(x; a, b) ♥ t ♥♦♠♣t t ♥t♦♥ ♥ ② B(x; a, b) =

∫ x

0wa(1 − w)bdw r♦♠ t ♣r♦s ①♣rss♦♥ ts s

strt♦rr t♦ ♦t♥ p(xb, z|ω)

Pr♦♦ ♦ Pr♦♣♦st♦♥ ♦r t ♠♦ t ℓkb − 1 ♠♦s t st ♠♦s♦t♦♥s r ♥♦♥ ♥ ♥ ② δ

kb t♥ t ♦♥t♦♥ ♣r♦t② ♦ xb ♦r ♠♦ t ℓkb ♠♦s s

p(xb|z, ℓkb, δ⊖

kb, εkb) =1

♠b − ℓkb + 1

τ∈1,...,♠b\δ⊖kb

p(xb|z, ℓkb, δ⊖

kb, τ,α⊖kb, εkb),

s ② ♣♣r♦①♠t♥ ts s♠ ② ts ♠①♠♠ ♠♥t ♦t♥ tt

p(xb|z, ℓkb, δ⊖

kb, εkb) ≈1

♠b − ℓkb + 1p(xb|z, ℓkb, δkb,α⊖

kb, εkb).

② s♥ ♠♠ ♦t♥ tt

p(xb|z, ℓkb, δ⊖

kb, εkb)

p(xb|z, ℓ⊖kb, δ⊖

kb, ε⊖kb)

≈ (♠b − ℓkb + 1)♥ℓkb−1

kb −1

(♠b − ℓkb)♥ℓkbkb

(εℓkbkb )♥(ℓkb)

kb (1− εℓkbkb )♥ℓkbkb .

Page 210: Model-based clustering for categorical and mixed data sets

♦♠♣tt♦♥ ♦ t ♥trt ♦♠♣tt ♦♦ ♦ t ♠①tr ♠♦

♦ ♠t♥♦♠ strt♦♥s ♣r ♠♦s

s p(xb|z, ℓkb = 0) = (mb)−nk ② ♣♣②♥ rrs② t ♣r♦s ①♣rss♦♥ ♦t♥ tt

p(xb|z, ℓkb, εkb) ≈(

1

♠b − ℓkj

)♥ℓkbkb

ℓkb∏

h=1

(εhkb)♥(h)kb (1− εhkb)

♥hkb

♠b − h+ 1.

Page 211: Model-based clustering for categorical and mixed data sets
Page 212: Model-based clustering for categorical and mixed data sets

♣♣♥①

♣♣♥① ♦ Prt

♥tt② ♦ t ♠①tr ♠♦ ♦ ss♥

♥ ♦st strt♦♥s

Pr♦♣♦st♦♥ ♠①tr ♠♦ ♦ ss♥ ♥ ♦st strt♦♥s s♥r② ♥t

Pr♦♦ ♣♣♦s tr r t♦ ♠①tr ♠♦s ♦ ss♥ ♥ ♦st strt♦♥s♥♦t ② p(xi;θ) ♥ p(xi; θ) s tt

∀xi,g∑

k=1

πkp(xi;αk) =

g∑

k=1

πkp(xi; αk), 0 < πk, πk ≤ 1,

g∑

k=1

πk =

g∑

k=1

πk = 1.

♠ s t♦ ♣r♦ tt θ = θ ♠♦♥strt♦♥ s s♣t ♥ t♦ ♣rts ♥ trst ♦♥ s♦ t qt② ♦ t ss♥ strt♦♥s ♣r♠trs ♥ ♦ t♣r♦♣♦rt♦♥s ♥ t s♦♥ ♦♥ s♦ t qt② ♦ t ♣r♠trs ♦ t ♦strrss♦♥s

♦♥t♥♦s ♣r♠trs ♥ ♣r♦♣♦rt♦♥s❲ s♠ qt♦♥ ♦r t ♣♦ss s ♦ x

i s♦ ♦t♥ tt

∀x

i ,

g∑

k=1

πkφ(x

i ;µk,Σk) =

g∑

k=1

πkφ(x

i ; µk, Σk), 0 < πk, πk ≤ 1,

g∑

k=1

πk =

g∑

k=1

πk = 1.

♥tt② ♦ t ♥t ss♥ ♠①trs ♠♦s s ❬❪ ♦r t♥rt s ♥ ❬❨❪ ♦r t ♠trt s ♥♦s tt g = gπk = πk µk = µk ♥ Σk = Σk

Pr♠trs ♦ t ♦st rrss♦♥st s r ❬❪ tt ∀j = 1 + c, . . . , e ∀(x

i ,xji )

g∑

k=1

fk(x

i )p(xji |x

i ;βkj) =

g∑

k=1

fk(x

i )p(xji |x

i ; βkj)

Page 213: Model-based clustering for categorical and mixed data sets

♣♣♥① ♣♣♥① ♦ Prt

♥♦s tt βkj = βkj r fk(x

i ) = πkφ(x

i ;µk,Σk) t♥ ♠①tr ♠♦♦ ss♥ ♥ ♦st strt♦♥s s ♥tt t t♦r ♦ s③ c ♥♦t ② yi = (y1, . . . , yc) r t ♠♥tsr ③r♦ ①♣t t ♠♥t j′ s q t♦ a ❲t♦t ♦ss ♦ ♥rt② ♦♥sr tt t fk(yi ) r ♦rr s tt Σ−1

k (j′, j′) < Σ−1k+1(j

′, j′)r♦♠ qt♦♥ tt

g∑

k=1

fk(y

i )α1(βkj|yi ) =g∑

k=1

fk(y

i )α1(βkj|yi ),

t(

α1(βkj|yi ))−1

= 1 +∑mj

h=2 exp(β0hkj + βj

′hkj a) ❲ t ♦

qt♦♥ ② f1(yi )α1(β1j|yi ) ts

1 +

g∑

k=2

fk(y

i )α1(βkj|yi )f1(yi )α1(β1j|yi )

=α1(β1j|yi )α1(β1j|yi )

+

g∑

k=2

fk(y

i )α1(βkj|yi )f1(yi )α1(β1j|yi )

.

tt♥ a → ∞∑g

k=2

fk(y

i )α1(βkj |y

i )

f1(yi )α1(β1j |y

i )= 0 ♥

∑gk=2

fk(y

i )α1(βkj |y

i )

f1(yi )α1(β1j |y

i )= 0 s♥

t fk(yi ) r ♦rr ❲t♦t ♦ss ♦ ♥rt② mj > 2 ss♠ ttβj

′h1j > βj

′h+11j 1 < h < mj

lima→∞

α1(β1j|yi )α1(β1j|yi )

= lima→∞

exp(

(βj′2kj − βj

′2kj )a+ (β02

kj − β02kj ))

= 1.

♦ qt♦♥ ♥♦s tt βj′2kj = βj

′2kj ♥ β02

kj = β02kj ② r♣t♥

ts r♠♥t ♦r h = 3, . . . ,mj t♥ ♦r j = 1, . . . , ♦♥ ttβ1j = β1j ② r♣t♥ ts r♠♥t ♦r j = 1 + , . . . , + t♥ ♦rk = 2, . . . , g ♦♥ tt qt♦♥ s tr t♥ θ = θ

♥tt② ♦ t ♠①tr ♠♦ ♦ ss♥

♦♣s

♠♦ ♥tt② s ♣r♦ ② t♦ ♣r♦♣♦st♦♥s rst ♣r♦♣♦st♦♥♣r♦s t ♠♦ ♥tt② ♥ t rs r ♦♥t♥♦s ♥♦r ♥trs ♣r♦♣♦st♦♥ ♣rs♥ts t rs♦♥♥ ♥ s♠♣ s s♥ t ♦s ♥♦t ♦♥srt ♦r♥ rs s♦♥ ♣r♦♣♦st♦♥ ♣r♦s tt t ♠♦ rqrs t st♦♥ ♦♥t♥♦s ♦r ♥tr r t♦ ♥t

Pr♦♣♦st♦♥ ♥tt② t ♦♥t♥♦s ♥ ♥tr rs ♠①tr ♠♦ ♦ ss♥ ♦♣s s ② ♥t ❬❪ t rs r♦♥t♥♦s ♥ ♥tr ♦♥s t ♠r♥ strt♦♥s ♦ t ♦♠♣♦♥♥ts rss♥ ♦r P♦ss♦♥ strt♦♥s s

∀x ∈ Rc × N

d,

g∑

k=1

πkp(x;αk) =

g′∑

k=1

π′kp(x;α

′k)

⇒ g = g′, π = π′, α = α′.

Page 214: Model-based clustering for categorical and mixed data sets

♥tt② ♦ t ♠①tr ♠♦ ♦ ss♥ ♦♣s

Pr♦♦ ♥tt② ♦ t ♠trt ss♥ ♠①tr ♠♦s ♥ ♦ t♥rt P♦ss♦♥ ♠①tr ♠♦ ❬ ❨❪ ♥♦s tt ♠♣s

g = g′, π = π′, βkj = β′kj ♥ Γk = Γ

′k.

❲ ♥♦ s♦ tt Γk = Γ′k ♥ Γk = Γ

′k

t j ∈ 1, . . . , c ♥ h ∈ c + 1, . . . , e ❲ ♥♦t ② ρk = Γk(j, h)

ρ′k = Γ′k(j, h) vk = Φ−1

1 (P (xj;βkj)) εk(xj) = πk

φ1(vk)σkj

ak =b⊕k (xj)−ρkvk√

1−ρ2k♥

a′k =b⊕k (xj)−ρ′kvk√

1−ρ′2k ❲t♦t ♦ss ♦ ♥rt② ♦rr t ♦♠♣♦♥♥ts s s

σkj > σk+1j ♥ σkj = σk+1j t♥ µkj > µk+1j t♥ ♠♣s tt

1 +

g∑

k=2

(εk(xj)Φ(ak))/(ε1(x

j)Φ(a1)) =

g∑

k=1

εk(xj)Φ(a′k)/(ε1(x

j)Φ(a1)).

t γt = (xj, xh) ∈ R× N : a1 = t ♥ tt♥ xh → ∞ s s (xj, xh) ∈ γt

∀t,∫ a′1tφ(u)du

Φ(t)= 0.

s a′1 = a1 s♦ ρ′1 = ρ1 ♣t♥ ts r♠♥t ♦r k = 2, . . . , g ♥ ♦r t♦♣s (j, h) ♦♥ tt Γk = Γ

′k

❲♥ ♦t rs r ♥tr s t s♠ r♠♥t t γ(t,ξ) = (xj, xh) ∈N × N : a1 ∈ B(t, ξ) ♦t tt ρ1 6= ρ′1 t♥ ∃n0 s s ∀xj > n0 a

′1 > t + ξ

tt♥ xh → ∞ s s (xj, xh) ∈ γ(t,ξ) ♦t♥ t ♦♦♥ ♦♥trt♦♥

∫ a′1t+ξ

φ(u)du

Φ(t− ξ)= 0 ♥

∫ a′1t+ξ

φ(u)du

Φ(t− ξ)> 0.

♦ a′1 = a1 t♥ ρ1 = ρ′1 ♣t♥ ts r♠♥t ♦r k = 2, . . . , g ♥ ♦r t♦♣s (j, h) ♦♥ tt Γk = Γ

′k

Pr♦♣♦st♦♥ ♥tt② ♦ t ♠①tr ♠♦ ♦ ss♥ ♦♣s ♠①tr ♠♦ ♦ ss♥ ♦♣s s ② ♥t ❬❪ t st ♦♥ r s ♦♥t♥♦s ♦r ♥tr

Pr♦♦ ♥ ts ♣r♦♦ ♦♥sr ♦♥② ♦♥ ♦♥t♥♦s r ♥ t♦ ♥r② rs ♦s② t s♠ rs♦♥♥ ♥ ①t♥ t♦ t ♦tr ss ❲ ♥♦s♦ tt Γk = Γ

′k ♥ Γk = Γ

′k

t j = 1 ♥ t h ∈ 2, 3 ❲ ♥♦t ρk = Γk(j, h) ρ′k = Γ′k(j, h) vk =

Φ−11 (P (xj;βkj)) εk(x

j) = πkφ(vk;0,1)σkj

ak =b⊕k (xj)−ρkvk√

1−ρ2k♥ a′k =

b′⊕k (xj)−ρ′kvk√1−ρ′2k

❲t♦t

♦ss ♦ ♥rt② ♦rr t ♦♠♣♦♥♥ts s s σkj > σ[k+1]j ♥ σkj = σ[k+1]j

t♥ µkj > µ[k+1]j ♦t tt ♠♣s tt

1 +

g∑

k=2

(εk(xj)Φ(ak))/(ε1(x

j)Φ(a1)) =

g∑

k=1

εk(xj)Φ(a′k)/(ε1(x

j)Φ(a1)).

Page 215: Model-based clustering for categorical and mixed data sets

♣♣♥① ♣♣♥① ♦ Prt

tt♥ x1 → ∞ ♥ ss♠♥ tt ρk > 0 t♥ Φ(a′k)

Φ(ak)= 1 ♦ s♥(ρk) = s♥(ρ′k).

② ♥♦t♥ κ = lima→∞

φ(a)Φ(a)

♥ tt♥ x1 → ∞ κ 1κ

φ(a′k)

φ(ak)= 1. s a′1 = a1 s♦ ρ′1 = ρ1

♥ b⊕k (xj) = b′⊕k (xj) s♦ βkh = β′

kh♦t tt t s♠ rst ♥ ♦t♥ ② t♥♥ x1 t♦ −∞ s ρk < 0

♣t♥ ts r♠♥t ♦r k = 2, . . . , g ♥ ♦r t ♦♣s (j, h) ♦♥tt Γk = Γ

′k t♥ Γk = Γ

′k

Page 216: Model-based clustering for categorical and mixed data sets

♦r♣②

❬❪ ③③♥ ♥ ❲ ♦♠♥ ♦♦ t s♦♠ t ♦♥ t t②sr ♣♣ ttsts

❬❪ P ♥rs♥ ♦r♥ ♥ ♥ ttst ♠♦s s ♦♥ ♦♥t♥ ♣r♦sss ♣r♥r rs ♥ ttsts ♣r♥r❱r ❨♦r

❬❪ ♠ ♥ ② ♠♥ str♥ ♦rt♠ ♦r ♠① ♥♠r♥ t♦r t t ♥♦ ♥♥r♥

❬r❪ rst t♦r t ♥②ss ♦♠ ♦♥ ❲② ♦♥s

❬❪ ♥♦r♠t♦♥ t♦r② ♥ ♥ ①t♥s♦♥ ♦ t ♠①♠♠♦♦ ♣r♥♣ ♥ ♦♥ ♥tr♥t♦♥ ②♠♣♦s♠ ♦♥ ♥♦r♠t♦♥ ♦r② ss♦r ♣s é♠ ó ♣st

❬❪ ♠♥ ts ♥ ♦s ♥tt② ♦ ♣r♠trs♥ t♥t strtr ♠♦s t ♠♥② ♦sr rs ♥♥s♦ ttsts

❬❱❪ rtr ♥ ❱ssts ♠♥s ♥ts ♦ rs♥ ♥ Pr♦♥s ♦ t t♥t ♥♥ s②♠♣♦s♠ ♦♥ srt ♦rt♠s ♣s ♦t② ♦r ♥str♥ ♣♣ t♠ts

❬❪ r ♣r♦st str♥ ♠♦ ♦r rs ♦ ♠① t②♣t② ♥ ♥tt②

❬❪ ♦②r♦♥ ♥ r♥t ♦s str♥ ♦ ♠♥s♦♥ t r ♦♠♣tt♦♥ ttsts ♥ t ♥②ss

❬❪ r♥ ① ♥ ♦rt ssss♥ ♠①tr ♠♦ ♦rstr♥ t t ♥trt ♦♠♣t ♦♦ Pttr♥ ♥②ss♥ ♥ ♥t♥ r♥st♦♥s ♦♥

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♦r♣②

❬❪ r♥ ① ♥ ♦rt ①t ♥ ♦♥t r♦ t♦♥s ♦ ♥trt ♦♦s ♦r t t♥t ss ♠♦ ♦r♥ ♦ttst P♥♥♥ ♥ ♥r♥

❬❪ ♥ ♥ ♥tr ♥ ♦rt♠ ♦rs♠ ♥ ♥♦♥♣r♠tr st♠t♦♥ ♥ ♠trt ♠①trs ♦r♥♦ ♦♠♣tt♦♥ ♥ r♣ ttsts

❬❨❪ ♥ ♥tr ♥ ❨♦♥ ♠①t♦♦s ♥ ♣ ♦r ♥②③♥ ♥t ♠①tr ♠♦s ♦r♥ ♦ ttst♦tr

❬r❪ P r♥ sr② ♦ str♥ t ♠♥♥ t♥qs ♥ r♦♣♥♠t♠♥s♦♥ t ♣s ♣r♥r

❬❪ ♦♥♥♥s rt ♠r ♥ st③ ♠r ♣t♠③t♦♥ t♦rt ♥ ♣rt s♣ts

❬❪ r♥ ♥r② ♥ t ♠①♠♠ ♦♦ st♠t♦♥ ♦ ♥rt ss♥ ♠①trs ♦r r♦♣ t ♥ ♦r ♦ t ♦rt♠ ♥♥♥ ♦r♥ ♦ ttsts

❬❪ r♥ ♥ ♦r♠ ss♥ Prs♠♦♥♦s str♥ ♦s ♥r♥t ♥ t ② Pr♦t♦♥ ttsts ♥ ♦♠♣t♥♣ ♥ ♣rss

❬❪ r♥r ♦ ♥ ❳ ♥ ♦♥ ♦r♥ ♠trs ♥ tr♠s ♦ st♥r t♦♥s ♥ ♦rrt♦♥s t ♣♣t♦♥t♦ sr♥ ttst ♥

❬♦③❪ ♦③♦♥ ♦ st♦♥ ♥ s ♥♦r♠t♦♥ rtr♦♥ t ♥r t♦r② ♥ ts ♥②t ①t♥s♦♥s Ps②♦♠tr

❬❪ ♥ ♥ tr② ♦s ss♥ ♥ ♥♦♥ss♥ str♥ ♦♠trs ♣s

❬❪ r♥t ♥ ♦♥ rt♦♥ ♦ t ♦rt♠ Prss ♣s♦♥ ♦rt♠ ♦♠♣tt♦♥ ttsts ♥ t ♥②ss

❬+❪ P r② tr② ① ♦ ♥ ♦ttr♦ ♦♠♥♥ ♠①tr ♦♠♣♦♥♥ts ♦r str♥ ♦r♥ ♦ ♦♠♣tt♦♥♥ r♣ ttsts

❬r❪ ❱ rt♥♦ Prs♦♥ ♦♠♠♥t♦♥ s♦r s♠

❬+❪ P ♦rt③ rr ♠ t♦s ♥ s ♦♥ ♥ ♣rr♥s ② t ♠♥♥ r♦♠ ♣②s♦♠ ♣r♦♣rtss♦♥ ♣♣♦rt ②st♠s

Page 218: Model-based clustering for categorical and mixed data sets

♦r♣②

❬+❪ ① ♦t t ♥ ♦rt♠s ♦r ♠①trsttst ♥ ♥♠r s♣ts ♣♣♦rt r ♥r

❬❪ ① ♥ ♦t st♦st ♣♣r♦①♠t♦♥ t②♣ ♦rt♠ ♦r t ♠①tr ♣r♦♠ t♦sts ♥ ♥tr♥t♦♥ ♦r♥♦ Pr♦t② ♥ t♦st Pr♦sss

❬❪ ① ♥ ♦rt str♥ rtr ♦r srt t ♥t♥t ss ♠♦s ♦r♥ ♦ sst♦♥

❬❪ ① ♥ ♦rt sst♦♥ ♦rt♠ ♦r str♥♥ t♦ st♦st rs♦♥s ♦♠♣tt♦♥ ttsts t ♥②ss

❬❪ ① ♥ ♦rt ss♥ ♣rs♠♦♥♦s str♥ ♠♦sPttr♥ ♦♥t♦♥

❬❪ ♥tr ♥ ♥ st♠t♦♥ ♦r ♦♥t♦♥♥♣♥♥ ♠trt ♥t ♠①tr ♠♦s

❬❪ ♥t ❱ ♥t③♠♦♥t ♥ r♦ rt♦♦♥ r♦tt♦♥ ♥P❳ ♥s ♥ t ♥②ss ♥ sst♦♥

❬❪ ♦ ♥ ♣♣r♦①♠t♥ srt ♣r♦t② strt♦♥st ♣♥♥ trs ♥♦r♠t♦♥ ♦r② r♥st♦♥s ♦♥

❬❪ P ♦rt③ ♥ ♦rs t ♠♥♥ ♣♣r♦ t♦ ♣rt ♦rst rss♥ ♠t♦r♦♦ t

❬❪ ♦rt ♥ r st♠t♦♥ ♥ srt ♣r♠tr ♠♦s ttst ♥

❬❩+❪ ♣ ö♦♣ ❩♥ t ♠s♣rs r♥♥♦♠ ♣rss ♠r

❬❩❪ ③r♥ ♥ ❩r③② ♣♣t♦♥ ♦ r♦ sts ♥ t ♣rs♠♣t ♥♦ss ♦ r♥r② s②st♠ sss rt ♥t♥ ♥ rt② ♥ ♦♠♣t♥ ②st♠s t ♥tr♥t♦♥ ♦♥r♥Pr♦♥s ♣s

❬t❪ tr♥♦ ♥ rt ss t ♦♥ ♥ ♥♥ ♦♥t♦♥

❬❪ P ♠♣str r ♥ ♥ ①♠♠ ♦♦ r♦♠♥♦♠♣t t t ♦rt♠ ♦r♥ ♦ t ♦② ttst♦t② rs t♦♦♦ ♣s

Page 219: Model-based clustering for categorical and mixed data sets

♦r♣②

❬❪ ♥ r♣② ♥ ♦♥② ❯s♥ ♥ t t♦ ♣t sst♦♥ rs t ♣♣t♦♥s ♥ ♦♦ t♥tt② sts♦r♥ ♦ t ♦② ttst ♦t② rs ♣♣ ttsts

❬❪ s♣♥ ♥ ♥♠♥ ❯s♥ t♥t ss ♦s t♦rtr③ ♥ ssss t rr♦r ♥ srt sr♠♥ts ♦♠trs ♣♣

❬❪ rtt ♥ s ♥ t str ♥②ss♦♥♦♥ ② t♦♥

❬❪ rtt ♥t ♠①tr ♠♦ ♦r t str♥ ♦ ♠①♠♦t ttsts Pr♦t② ttrs

❬♦r❪ ♦rs②t P srs tt♣rs♠❯P sr t

❬♦r❪ ♦r♠♥♥ ♥r ♦st t♥t ss ♥②ss ♦r ♣♦②t♦♠♦s t♦r♥ ♦ t ♠r♥ ttst ss♦t♦♥

❬❪ r② ♥ tr② ❯ rs♦♥ ♥ ♣ ♦r♥♦r♠ ♠①tr ♠♦♥ ♥ ♠♦s str♥ ♥ r♣♦rt ♦♠♥t

❬❲❪ r ②♥ ♥ ❲②s ②s♥ ♠♦ st♦♥ ♦r tt♥t ♣♦st♦♥ str ♠♦ ♦r s♦ ♥t♦rs r❳ ♣r♣r♥tr❳

❬❪ rürt♥ttr ♥t ♠①tr ♥ r♦ st♥ ♠♦s♣r♥r

❬❪ rürt♥ttr ♥t ①tr ♥ r♦ t♥ ♦s

❬❲❪ r ♥ ❲②s st♠t♥ t ♥ r ttstr♥

❬❪ ♥st ♥ r r②t♥ ②♦ ②s ♥t t♦ ♥♦♦t ♦♣ ♠♦♥ t r r t♦ s ♦r♥ ♦ ②r♦♦♥♥r♥

❬❪ ♦♥ ♥ r♣② ①tr ♦ t♥t trt ♥②③rs ♦r ♠♦s str♥ ♦ t♦r t ttsts ♥ ♦♠♣t♥ ♣s

❬❪ ♦rt ♥ ♦ str♥ t r♥♦ ♠①tr ♠♦s ♦♠♣rs♦♥ ♦ r♥t ♣♣r♦s ♦♠♣tt♦♥ ttsts t ♥②ss

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❬❪ ♦rt ♥ t♥t ♦ ♠♦ ♦r ♦♥t♥♥② t ♦♠♠♥t♦♥s ♥ ttsts♦r② ♥ t♦s

❬♦♦❪ ♦♦♠♥ ①♣♦rt♦r② t♥t strtr ♥②ss s♥ ♦t ♥t ♥ ♥♥t ♠♦s ♦♠tr

❬♦❪ ♦t ❯tst♦♥ s ♠♦ès ♠é♥ ♣♦r sst♦♥ t♦♠tq ♦♥♥és ♦r♥s P tss ❯♥rsté ♥♦♦ ♦♠♣è♥

❬♦❪ ♦rt t ♥②ss ♦♠ ❲②

❬r❪ P r♥ ♥ s ♦ t ♦r ♣♥③ ♦♦ st♠t♦♥ ♦r♥ ♦ t ♦② ttst ♦t② rs t♦♦♦ ♣s

❬r❪ P r♥ rs ♠♣ r♦ ♥ ♦♥t r♦ ♦♠♣tt♦♥♥ ②s♥ ♠♦ tr♠♥t♦♥ ♦♠tr

❬❪ rt ♥ P r ♦♠♣r♥ ♣rtt♦♥s ♦r♥ ♦ sst♦♥

❬❪ ♥rs t♥t strtr ♠♦s t rt ts t♥♥t♦rs ♦ ♣♥♥ ♠♦s ♦♦♦ t♦s sr

❬r❪ r♣r ♦ ♣♥♥ t♥t strtr ♠♦s Ps②♦♠tr

❬❪ ♥ ❲ ♦♥ ♥ ❨ ♥ ♥ ①t♥s♦♥ ♦ ♠t♣ ♦rrs♣♦♥♥ ♥②ss ♦r ♥t②♥ tr♦♥♦s sr♦♣s ♦ rs♣♦♥♥ts Ps②♦♠tr

❬♥❪ ♥♥ t♦s ♦r ♠r♥ ss♥ ♠①tr ♦♠♣♦♥♥ts ♥s ♥ t ♥②ss ♥ sst♦♥

❬❪ ♥t ♥ ♦r♥s♥ ♦r② t♦s ①tr ♠♦ str♥ s♥ t ❯❳ ♣r♦r♠ str♥ ❩♥ ♦r♥♦ ttsts

❬❪ ♥t ♥ ♦r♥s♥ str♥ ♠① t ❲② ♥trs♣♥r② s t ♥♥ ♥ ♥♦ s♦r②

❬❲❪ P ♦ ❳ ♥ ❲♥r ♥♦r♠t♦♥ ♦♥s ♦r ss♥♦♣s r❳ ♣r♣r♥t r❳

❬♦❪ P ♦ ①t♥♥ t r♥ ♦♦ ♦r s♠♣r♠tr ♦♣st♠t♦♥ ♥♥s ♦ ♣♣ ttsts ♣s

Page 221: Model-based clustering for categorical and mixed data sets

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❬❪ ❩ ♥ ①t♥s♦♥s t♦ t ♠♥s ♦rt♠ ♦r str♥ rt ts t t♦r ❱s t ♥♥ ♥ ♥♦ s♦r②

❬❲❪ ♥tr ❲♥ ♥ P tt♠♥s♣rr ♥r♥ ♦r ♠①trs♦ s②♠♠tr strt♦♥s ♥♥s ♦ ttsts ♣s

❬❨❪ ♥ ♥ ❨ ♦ts ②s ♦t ♦ t♣ tr ♥tr♥t♦♥ ttst

❬❪ qs ♥ r♥ ♦s str♥ ♦r ♠trt♣rt r♥♥ t ♣♣♦rt r ♥r

❬❪ ♦r♥s♥ ♥ ♥t ①tr ♠♦ str♥ ♦ t sts tt♦r ♥ ♦♥t♥♦s rs ♥ Pr♦♥s ♦ t ♦♥r♥ ♦♠ ♣s

❬♦❪ ♦ trt ♠♦s ♥ ♠trt ♣♥♥ ♦♥♣ts ♦♠ Prss

❬P❪ qs ♥ Pr ♦s str♥ ♦ ♠trt ♥t♦♥ t ♦♠♣tt♦♥ ttsts ♥ t ♥②ss

❬r❪ r♥ ♦♥sst♥t st♠t♦♥ ♦ t ♦rr ♦ ♠①tr ♠♦s♥② r

❬❪ ♦s♠s ♥ rs ♦s str♥ s♥ ♦♣s t♣♣t♦♥s r❳ ♣r♥ts

❬❪ ♥ r ♥ ♥♦r♠t♦♥ ♥ s♥② ♥♥s ♦ t♠t ttsts

❬❪ rs ♥ ♦ts♦ ♥t ♠①trs ♦ ♠trt P♦ss♦♥ strt♦♥s t ♣♣t♦♥ ♦r♥ ♦ sttst P♥♥♥ ♥♥r♥

❬r❪ ♦s♣ rs ♥ t s♦rtst s♣♥♥♥ str ♦ r♣ ♥ ttr♥ ss♠♥ ♣r♦♠ Pr♦♥s ♦ t ♠r♥ t♠ts♦t②

❬r③❪ ❲ r③♥♦s ♦t♦♥ ♠♦ ♦r ♠①trs ♦ t♦r ♥♦♥t♥♦s rs ♦r♥ ♦ sst♦♥

❬❪ rs ♥ P s♠②rt③s ①t ②s♥ ♠♦♥ ♦r rtP♦ss♦♥ t ♥ ①t♥s♦♥s ttsts ♥ ♦♠♣t♥

❬❲❪ ss♥ ♥ ❲♥r ♥t st♠t♦♥ ♥ t rt♥♦r♠ ♦♣ ♠♦ ♥♦r♠ ♠r♥s r st ♦r r♥♦

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♦r♣②

❬❪ P t♦ r♠é ♥ ♠r♦s ②s♥ ♠t♦s ♦r r♣str♥ ♥ ♥s ♥ t ♥②ss t ♥♥ ♥ s♥ss♥t♥ ♣s ♣r♥r

❬❪ ♠♥♥ ♥ s ♦r② ♦ ♣♦♥t st♠t♦♥ ♦♠ ♣r♥r

❬+❪ rt ♦ ♥r♦♥t r♥ ① ♥ ♦rt ♠①♠♦ P ♦ t ♦s ❯♥s♣rs ♣rs ♥ ♠♣rs sst♦♥ ①♠♦ rr② Pr♣r♥t

❬❪ r♥ ♥ ❲ r③♥♦s ①tr s♣rt♦♥ ♦r ♠①♠♦ t ttsts ♥ ♦♠♣t♥

❬♦❪ ♦② st sqrs q♥t③t♦♥ ♥ P ♥♦r♠t♦♥ ♦r② r♥st♦♥s ♦♥

❬❪ ♠ ❲❨ ♦ ♥ ❨ ♦♠♣rs♦♥ ♦ ♣rt♦♥ r② ♦♠♣①t② ♥ tr♥♥ t♠ ♦ trt②tr ♦ ♥ ♥ sst♦♥ ♦rt♠s ♥ r♥♥

❬❱❪ r r♥ ♥ ❱ ❱♥ ♦s str♥♦r ♦♥t♦♥② ♦rrt t♦r t ♦r♥ ♦ sst♦♥

❬❱❪ r r♥ ♥ ❱ ❱♥ ♦s str♥♦r ♦♥t♦♥② ♦rrt t♦r t ♣♣♦rt rr

❬❱❪ r r♥ ♥ ❱ ❱♥ ♥t ♠①tr ♠♦ ♦♦♥t♦♥ ♣♥♥s ♠♦s t♦ str t♦r t ♠tt

❬❱❪ r r♥ ♥ ❱ ❱♥ ♦s str♥♦ ss♥ ♦♣s ♦r ♠① t ♠tt

❬❪ rr② ♥s♦♥ r♥ ♥ s ②s♥ ss♥♦♣ t♦r ♠♦s ♦r ♠① t ♦r♥ ♦ t ♠r♥ ttstss♦t♦♥

❬❪ ♥ ♦r♥ r♥♥ t ♠①trs ♦ trs ♦r♥♦ ♥ r♥♥ sr

❬❪ ♥ ♥ rs♥♥ ♦rt♠ ❲② rs ♥Pr♦t② ♥ ttsts ♣♣ Pr♦t② ♥ ttsts ❲②♥trs♥ ❨♦r

❬❪ r♥ ♥rs♥ ♥ P ♦rt ②s♥ ♠♦♥ ♥♥r♥ ♦♥ ♠①trs ♦ strt♦♥s ♥♦♦ ♦ sttsts

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♦r♣②

❬P❪ ♥ ♥ P ♥t ♠①tr ♠♦s ❲② rs ♥Pr♦t② ♥ ttsts ♣♣ Pr♦t② ♥ ttsts ❲②♥trs♥ ❨♦r

❬P❪ ♦st ♥ P♣♦r♦ t♥t ss ♠♦s ♦r ♠① rst ♣♣t♦♥s ♥ r♦♠tr② ♦♠♣tt♦♥ sttsts t♥②ss

❬t❪ té♥ t♥t r ②rs r ♦ ♦ ♥ ♥ ♠♦s♥s ♥ t♥t r ♠①tr ♠♦s

❬❪ s♥ ♥ ♥tr♦t♦♥ t♦ ♦♣s ♣r♥r

❬s❪ ❯ ss♦♥ ①♠♠ ♦♦ st♠t♦♥ ♦ t ♣♦②♦r ♦rrt♦♥♦♥t Ps②♦♠tr

❬❪ ♥ ♥ t trt ♦rrt♦♥ ♠♦s t ♠① srt ♥ ♦♥t♥♦s rs ♥♥s ♦ t♠t ttsts♣s

❬Pr❪ Pr③♥ ♥ st♠t♦♥ ♦ ♣r♦t② ♥st② ♥t♦♥ ♥ ♠♦♥♥s ♦ ♠t♠t sttsts

❬P❪ Ptt ♥ ♥ ♦♥ ♥t ②s♥ ♥r♥ ♦r ss♥ ♦♣ rrss♦♥ ♠♦s ♦♠tr

❬P❪ Prs♦♥ ♦♥trt♦♥s t♦ t ♠t♠t t♦r② ♦ ♦t♦♥P♦s♦♣ r♥st♦♥s ♦ t ♦② ♦t② ♦ ♦♥♦♥

❬P❪ P ♥ ♥ ♦st ♠①tr ♠♦♥ s♥ t tstrt♦♥ ttsts ♥ ♦♠♣t♥

❬❪ ❨ ♥ ♥ t♥r ♥♦♠ ts ♦s ♥ t♥tss ♥②ss ♦r t♥ r② ♦ ♥♦st sts ♦♠trs♣♣

❬❪ tr② ②♣♦tss tst♥ ♥ ♠♦ st♦♥ ♥ r♦ ♥♦♥t r♦ ♥ ♣rt ♣s ♣r♥r

❬❪ ♠♦♥r♥② ♦♥♣t rs♦♥ ♦ t ♠♥s ♦rt♠Pttr♥ ♦♥t♦♥ ttrs

❬❪ P ♦rt ♥ s ♦♥t r♦ sttst ♠t♦s ♦♠ tsr

❬❪ rs♦♥ ♥ P r♥ ♥ ②s♥ ♥②ss ♦ ♠①trs t♥ ♥♥♦♥ ♥♠r ♦ ♦♠♣♦♥♥ts t sss♦♥ ♦r♥ ♦ t♦② ttst ♦t② rs ttst t♦♦♦②

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❬❲❪ ♦ss♥ ♣ ♥ ❲♦s♦♥ ♦② ♣♥♥t t♥tss ♠♦s t ♦rts ♥ ♣♣t♦♥ t♦ ♥r r♥♥ ♥t ❯ ♦r♥ ♦ t ♦② ttst ♦t② rs ttsts♥ ♦t②

❬♦❪ P ♦rt ②s♥ ♦ r♦♠ s♦♥t♦rt ♦♥t♦♥st♦ ♦♠♣tt♦♥ ♠♣♠♥tt♦♥ ♣r♥r

❬+❪ ♦ss♥ ❨ ♦♥ rst ♦♠♥ ♥ ❲♦s♦♥ t♥t ss ♥②ss ♦ ♥r ♣r♦♠ r♥♥ ♥r♦♠ ♦♠♠♥t② s♠♣ ♦ ②r ♦s r ♥ ♦♦ ♣♥♥

❬❪ r③ st♠t♥ t ♠♥s♦♥ ♦ ♠♦ ♥♥s ♦ ttsts

❬❪ P ❳ ♦♥ ❨ ♥ ♥ s ①♠③t♦♥ ② ♣rts♥ ♦♦ ♥r♥ ♦r♥ ♦ t ♠r♥ ttst ss♦t♦♥

❬❪ ♠t ♥ st♠t♦♥ ♦ ♦♣ ♠♦s t srt ♠r♥s ②s♥ t ♠♥tt♦♥ ♦r♥ ♦ t ♠r♥ttst ss♦t♦♥

❬+❪ trss ♥s♦♣ st♦♥r s rs♥ ♥ ❯s♥ t♥t ss ♥②ss t♦ ♥t② ♣ttr♥s ♦ ♣tts sr ♣r♦s♦♥ ♥ rr trt♠♥t ♣r♦r♠s ♥ t s r♥ ♦♦ ♣♥♥

❬t❪ t♣♥s ♥ t st♥ ♥ ♠①tr ♠♦s ♦r♥ ♦ t ♦② ttst ♦t② rs ttst t♦♦♦②

❬t❪ t♣♥s ♥ t st♥ ♥ ♠①tr ♠♦s ♦r♥ ♦ t ♦② ttst ♦t② rs ttst t♦♦♦②

❬❪ r ♥tt② ♦ ♥t ♠①trs ♥♥s ♦ t♠tttsts

❬❪ r ♥tt② ♦ ♠①trs ♦ ♣r♦t ♠srs ♥♥s ♦t♠t ttsts

❬❱r❪ ❱r♠♥t t t♥t ss ♠♦s ♦♦♦ ♠t♦♦♦②

❬❱r❪ ❱r♠♥t t ♠①tr t♠ rs♣♦♥s t♦r② ♠♦s ♥♣♣t♦♥ ♥ t♦♥ tst♥ Pr♦♥s ♦ t t sss♦♥ ♦ t♥tr♥t♦♥ ttst ♥sttt s♦♥ P♦rt ♣s

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♦r♣②

❬❱❪ P ❱♥ tt♠ ♥ ♦t♥ rt ♠♥tt♦♥ ❯s♥ r♥trt② sr ②s♥ ♥r♥ t s♣t t♦ ①trs ♦ ♦♥r ♦s ♦r♥ ♦ sst♦♥

❬❱❪ ❱r♥ ♥ ♦♥ ♠♣ ♥ ♦② ♦♥r♥t ♠t♦s♦r rt♥ t ♦♥r♥ ♦ ♥② ♦rt♠ ♥♥♥♦r♥ ♦ ttsts

❬❲❪ ❲s ♥ ♦ ♥t ♥t ♠①trs ♦ ♦t♦♥ ♠♦s♦r str♥ ♠①♠♦ t ttsts ♥ ♦♠♣t♥

❬❲❪ ❲ ♥ t ♦♥r♥ ♣r♦♣rts ♦ t ♦rt♠ ♥♥s ♦ ttsts

❬❨❪ ❨♦t③ ♥ ♣r♥s ♥ t ♥tt② ♦ ♥t ♠①trs♥♥s ♦ t♠t ttsts

❬❩❪ ❩ ❩♠♦♠ ♥ s ♦ r♥ ♥st② st♠t♦♥ t♣♣t♦♥s t♦ ♦♥♦♠trs r❳ ♣r♣r♥t r❳