the university of north carolina at chapel hill sequential pattern mining comp 790-90 seminar bcb...

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL

Sequential Pattern Mining

COMP 790-90 Seminar

BCB 713 Module

Spring 2011

COMP 790-090 Data Mining: Concepts, Algorithms, and Applications

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Sequential Pattern Mining

Why sequential pattern mining?

GSP algorithm

FreeSpan and PrefixSpan

Boarder Collapsing

Constraints and extensions


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Sequence Databases and Sequential Pattern Analysis

(Temporal) order is important in many situationsTime-series databases and sequence databasesFrequent patterns (frequent) sequential patterns

Applications of sequential pattern miningCustomer shopping sequences:

First buy computer, then CD-ROM, and then digital camera, within 3 months.

Medical treatment, natural disasters (e.g., earthquakes), science & engineering processes, stocks and markets, telephone calling patterns, Weblog click streams, DNA sequences and gene structures

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What Is Sequential Pattern Mining?

Given a set of sequences, find the complete set of frequent subsequences

A sequence database A sequence : < (ef) (ab) (df) c b >

An element may contain a set of items.Items within an element are unorderedand we list them alphabetically.

<a(bc)dc> is a subsequence of <<a(abc)(ac)d(cf)>Given support threshold min_sup =2, <(ab)c> is

a sequential pattern

SID sequence

10 <a(abc)(ac)d(cf)>

20 <(ad)c(bc)(ae)>

30 <(ef)(ab)(df)cb>

40 <eg(af)cbc>


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Challenges on Sequential Pattern Mining

A huge number of possible sequential patterns are hidden in databasesA mining algorithm should

Find the complete set of patterns satisfying the minimum support (frequency) thresholdBe highly efficient, scalable, involving only a small number of database scansBe able to incorporate various kinds of user-specific constraints

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A Basic Property of Sequential Patterns: Apriori

A basic property: Apriori (Agrawal & Sirkant’94) If a sequence S is not frequent

Then none of the super-sequences of S is frequent

E.g, <hb> is infrequent so do <hab> and <(ah)b>

<a(bd)bcb(ade)>50

<(be)(ce)d>40

<(ah)(bf)abf>30

<(bf)(ce)b(fg)>20

<(bd)cb(ac)>10

SequenceSeq. IDGiven support threshold min_sup =2


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Basic Algorithm : Breadth First Search (GSP)

L=1

While (ResultL != NULL)

Candidate Generate

Prune

Test

L=L+1

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Finding Length-1 Sequential Patterns

Initial candidates: all singleton sequences<a>, , <c>, <d>, <e>, <f>, <g>, <h>

Scan database once, count support for candidates

<a(bd)bcb(ade)>50

<(be)(ce)d>40

<(ah)(bf)abf>30

<(bf)(ce)b(fg)>20

<(bd)cb(ac)>10

SequenceSeq. ID

min_sup =2

Cand Sup

<a> 3

 5

<c> 4

<d> 3

<e> 3

<f> 2

<g> 1

<h> 1

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The Mining Process

<a> <c> <d> <e> <f> <g> <h>

<aa> <ab> … <af> <ba> <bb> … <ff> <(ab)> … <(ef)>

<abb> <aab> <aba> <baa> <bab> …

<abba> <(bd)bc> …

<(bd)cba>

1st scan: 8 cand. 6 length-1 seq. pat.

2nd scan: 51 cand. 19 length-2 seq. pat. 10 cand. not in DB at all

3rd scan: 46 cand. 19 length-3 seq. pat. 20 cand. not in DB at all

4th scan: 8 cand. 6 length-4 seq. pat.

5th scan: 1 cand. 1 length-5 seq. pat.

Cand. cannot pass sup. threshold

Cand. not in DB at all

<a(bd)bcb(ade)>50

<(be)(ce)d>40

<(ah)(bf)abf>30

<(bf)(ce)b(fg)>20

<(bd)cb(ac)>10

SequenceSeq. ID

min_sup =2

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Generating Length-2 Candidates

<a> <c> <d> <e> <f>

<a> <aa> <ab> <ac> <ad> <ae> <af>

 <ba> <bb> <bc> <bd> <be> <bf>

<c> <ca> <cb> <cc> <cd> <ce> <cf>

<d> <da> <db> <dc> <dd> <de> <df>

<e> <ea> <eb> <ec> <ed> <ee> <ef>

<f> <fa> <fb> <fc> <fd> <fe> <ff>

<a> <c> <d> <e> <f>

<a> <(ab)> <(ac)> <(ad)> <(ae)> <(af)>

 <(bc)> <(bd)> <(be)> <(bf)>

<c> <(cd)> <(ce)> <(cf)>

<d> <(de)> <(df)>

<e> <(ef)>

<f>

51 length-2Candidates

Without Apriori property,8*8+8*7/2=92 candidates

Apriori prunes 44.57% candidates

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Pattern Growth (prefixSpan)

Prefix and Suffix (Projection)

<a>, <aa>, <a(ab)> and <a(abc)> are prefixes of sequence <a(abc)(ac)d(cf)>

Given sequence <a(abc)(ac)d(cf)>

Prefix Suffix (Prefix-Based Projection)

<a> <(abc)(ac)d(cf)>

<aa> <(_bc)(ac)d(cf)>

<ab> <(_c)(ac)d(cf)>

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Example

Sequence_id Sequence

10 <a(abc)(ac)d(cf)>

20 <(ad)c(bc)(ae)>

30 <(ef)(ab)(df)cb>

40 <eg(af)cbc>

An Example

( min_sup=2):

Prefix Sequential Patterns

<a> <a>,<aa>,<ab><a(bc)>,<a(bc)a>,<aba>,<abc>,<(ab)>,<(ab)c>,<(ab)d>,<(ab)f>,<(ab)dc>,<ac>,<aca>,<acb>,<acc>,<ad>,<adc>,<af>

 , <ba>, <bc>, <(bc)>, <(bc)a>, <bd>, <bdc>,<bf>

<c> <c>, <ca>, <cb>, <cc>

<d> <d>,<db>,<dc>, <dcb>

<e> <e>,<ea>,<eab>,<eac>,<eacb>,<eb>,<ebc>,<ec>,<ecb>,<ef>,<efb>,<efc>,<efcb>

<f> <f>,<fb>,<fbc>, <fc>, <fcb>

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PrefixSpan (the example to be continued)

Step1: Find length-1 sequential patterns;

<a>:4, :4, <c>:4, <d>:3, <e>:3, <f>:3

patternsupport

Step2: Divide search space;

six subsets according to the six prefixes;

Step3: Find subsets of sequential patterns;

By constructing corresponding projected databases and mine

each recursively.

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Example to be continued

Prefix Projected(suffix) databases Sequential Patterns

<a> <(abc)(ac)d(cf)>,

<(_d)c(bc)(ae)>,

<(_b)(df)cb>,

<(_f)cbc>

<a>,<aa>,<ab><a(bc)>,<a(bc)a>,<aba>,<abc>,<(ab)>,<(ab)c>,<(ab)d>,<(ab)f>,<(ab)dc>,<ac>,<aca>,<acb>,<acc>,<ad>,<adc>,<af>

Sequence_id Sequence Projected(suffix) databases

10 <a(abc)(ac)d(cf)> <a(abc)(ac)d(cf)>

20 <(ad)c(bc)(ae)> <(ad)c(bc)(ae)>

30 <(ef)(ab)(df)cb> <(ef)(ab)(df)cb>

40 <eg(af)cbc> <eg(af)cbc>

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Example

Find sequential patterns having prefix <a>:

1. Scan sequence database S once. Sequences in S containing <a> are projected w.r.t <a> to form the <a>-projected database.

2. Scan <a>-projected database once, get six length-2 sequential patterns having prefix <a> :

<a>:2 , :4, <(_b)>:2, <c>:4, <d>:2, <f>:2

<aa>:2 , <ab>:4, <(ab)>:2, <ac>:4, <ad>:2, <af>:2

3. Recursively, all sequential patterns having prefix <a> can be further partitioned into 6 subsets. Construct respective projected databases and mine each.

e.g. <aa>-projected database has two sequences :

<(_bc)(ac)d(cf)> and <(_e)>.

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PrefixSpan Algorithm

PrefixSpan(, i, S|)

1. Scan S| once, find the set of frequent items b such that

• b can be assembled to the last element of to form a sequential pattern; or

• can be appended to to form a sequential pattern.

2. For each frequent item b, appended it to to form a sequential pattern ’, and output ’;

3. For each ’, construct ’-projected database S|’, and call PrefixSpan(’, i+1,S|’).

Main Idea: Use frequent prefixes to divide the search space and to project sequence databases. only search the relevant sequences.


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Approximate match

When you observe d1Spread count as

d1: 90%, d2: 5%, d3: 5%

Compatibility Matrix


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Match

The degree to which pattern P is retained/reflected in S

M(P,S) = P(P|S)= C(p,s) when when lS=lP

M(P,S) = max over all possible when lS>lP

ExampleP S M

d1d1 d1d3 0.9*0

d1d2 d1d2 0.9*0.8

d1d2 d1d3 0.9*0.05

d1d2 d2d3 0.1*0.05

d1d2 d1d2d3 0.9*0.8


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Calculate Max over all

Dynamic ProgrammingM(p1p2..pi, s1s2…sj)= Max of

M(p1p2..pi-1, s1s2…sj-1) * C(pi,sj)

M(p1p2..pi, s1s2…sj-1)

O(lP*lS)

When compatibility Matrix is sparse O(lS)


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Match in D

Average over all sequences in D


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Spread of match

If compatibility matrix is identity matrixMatch = support


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Anti-Monotone

The match of a pattern P in a symbol sequence S is less than or equal to the match of any subpattern of P in SThe match of a pattern P in a sequence database D is less than or equal to the match of any subpattern of P in DCan use any support based algorithm

More patterns match so require efficient solutionSample based algorithms

Border collapsing of ambiguous patterns


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Chernoff Bound

Given sample size=n, range R,with probability 1-true value: = sqrt([R2ln(1/)]/2n)

Distribution freeMore conservativeSample size : fit in memoryRestricted spread :

For pattern P= p1p2..pL

R=min (match[pi]) for all 1 i L

Frequent Patterns

min_match +

min_match -

Infrequent patterns


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Algorithm

Scan DB: O(N*min (Ls*m, Ls+m2))

Find the match of each individual symbol

Take a random sample of sequences

Identify borders that embrace the set of ambiguous patterns O(mLp * |S| * Lp * n)

Min_match existing methods for association rule mining

Locate the border of frequent patterns in the entire DB

via border collapsing


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Border Collapsing

If memory can not hold the counters of all ambiguous patternsProbe-and-collapse : binary searchProbe patterns with highest collapsing power until memory is filledIf memory can hold all patterns up to the 1/x layer

the space of ambiguous patterns can be narrowed to at least 1/x of the original onewhere x is a power of 2

If it takes a level-wise search y scans of the DB, only O(logxy) scans are necessary when the border collapsing technique is employed


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Periodic Pattern

Full periodic patternABC ABC ABC

Partial periodic patternABC ADC ACC ABC

Pattern hierarchyABC ABC ABC DE DE DE DE ABC ABC ABC DE DE DE DE ABC ABC ABC DE DE DE DE


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Periodic Pattern

Recent AchievementsPartial Periodic Pattern

Asynchronous Periodic Pattern

Meta Pattern

InfoMiner/InfoMiner+/STAMP


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Clustering Sequential Data

CLUSEQ

ApproxMAP

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