cara pemakaian weka
TRANSCRIPT
![Page 1: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/1.jpg)
Introduction to Datamining using WEKA
Anto Satriyo Nugroho
Center for Information & Communication Technology Agency for the Assessment & Application of Technology, Indonesia
Email: [email protected]
![Page 2: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/2.jpg)
2
Practicing WEKA
• What is WEKA ? • Formatting the data into ARFF • Klasifikasi
– Tahapan membangun classifier – Contoh kasus : Klasifikasi bunga iris – Tahapan membangun classifier – Merangkum hasil eksperimen k-Nearest Neighbor Classifier – Eksperimen memakai classifier yang lain (JST, SVM) – Classification of cancers based on gene expression – Parkinson Disease Detection
• K-Means Clustering
![Page 3: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/3.jpg)
3
What is WEKA ?
• Machine learning/data mining software written in Java (distributed under the GNU Public License)
• Used for research, education, and applications • Complements “Data Mining” by Witten & Frank • Main features:
– Comprehensive set of data pre-processing tools, learning algorithms and evaluation methods
– Graphical user interfaces (incl. data visualization) – Environment for comparing learning algorithms
• Weka versions – WEKA 3.4: “book version” compatible with description in data mining
book – WEKA 3.5: “developer version” with lots of improvements
![Page 4: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/4.jpg)
4
Formatting Data into ARFF (Attribute Relation File Format)
@relation iris @attribute sepallength real @attribute sepalwidth real @attribute petallength real @attribute petalwidth real @attribute class {Iris-setosa, Iris-versicolor, Iris-virginica} @data 5.1,3.5,1.4,0.2,Iris-setosa 4.9,3.0,1.4,0.2,Iris-setosa … 7.0,3.2,4.7,1.4,Iris-versicolor 6.4,3.2,4.5,1.5,Iris-versicolor … 6.3,3.3,6.0,2.5,Iris-virginica 5.8,2.7,5.1,1.9,Iris-virginica …
![Page 5: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/5.jpg)
5
Practicing WEKA
• What is WEKA ? • Formatting the data into ARFF • Klasifikasi
– Tahapan membangun classifier – Contoh kasus : Klasifikasi bunga iris – Tahapan membangun classifier – Merangkum hasil eksperimen k-Nearest Neighbor Classifier – Eksperimen memakai classifier yang lain (JST, SVM) – Classification of cancers based on gene expression – Parkinson Disease Detection
• K-Means Clustering
![Page 6: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/6.jpg)
6
Tahapan membangun Classifier
1. Tentukan manakah informasi yang merupakan (a) attribute/feature (b) class (c) training & testing set (d) skenario pengukuran akurasi
2. Tentukan kombinasi parameter model, dan lakukan proses pelatihan memakai training set
3. Ukurlah akurasi yang dicapai dengan testing set 4. Ubahlah parameter model, dan ulang kembali mulai dari
step 2, sampai dicapai akurasi yang diinginkan
![Page 7: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/7.jpg)
7
Contoh Kasus : Klasifikasi bunga iris
• Data set yang paling terkenal • Author: R.A. Fisher • Terdiri dari 3 kelas, masing-masing
memiliki 50 samples (instances) • Attribute information:
– Sepal (kelopak) length in cm – sepal width in cm – Petal (mahkota) length in cm – petal width in cm – class: (1) Iris Setosa (2) Iris
Versicolour (3)Iris Virginica • URL: http://archive.ics.uci.edu/ml/
datasets/Iris
![Page 8: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/8.jpg)
8
Flower’s parts
![Page 9: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/9.jpg)
9
Tahapan membangun Classifier
1. Tentukan manakah informasi yang merupakan (a) attribute/feature : sepal length (panjang kelopak) sepal width (lebar kelopak) petal length (panjang mahkota) petal width (lebar mahkota) (b) class: iris setosa iris versicolor iris virginica
(c) training & testing set training set : 25 instances/class testing set: 25 instances/class (d) skenario pengukuran akurasi
![Page 10: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/10.jpg)
Step by Step klasifikasi
10
![Page 11: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/11.jpg)
11
Open file “iris-‐training.arff”
![Page 12: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/12.jpg)
12
sta3s3cal informa3on of “sepallength”
Klik pada Classify untuk memilih Classifier algorithm
![Page 13: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/13.jpg)
13
Klik pada “Choose” untuk memilih Classifier algorithm
![Page 14: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/14.jpg)
14
SMO ( implementasi SVM)
Naïve Bayes
![Page 15: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/15.jpg)
15
IB1 : 1-‐Nearest Neighbor Classifier) IBk : k-‐Nearest Neighbor Classifier
![Page 16: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/16.jpg)
16
Mul3layer Perceptron (Jaringan Syaraf Tiruan)
![Page 17: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/17.jpg)
17
SMO singkatan dari Sequen3al Minimal Op3miza3on. SMO adalah implementasi SVM Mengacu pada paper John PlaQ
![Page 18: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/18.jpg)
18
Decision Tree J48 (C4.5)
![Page 19: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/19.jpg)
19
Misalnya kita pilih IBk : k-‐Nearest Neighbor Classifier
![Page 20: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/20.jpg)
20
Selanjutnya pilihlah skenario Pengukuran akurasi. Dari 4 Op3ons yang diberikan, pilihlah “Supplied test set” dan klik BuQon “Set” untuk memiilih Tes3ng set file “iris-‐tes3ng.arff”
![Page 21: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/21.jpg)
21
Tahapan membangun Classifier
Iris-‐training.arff
Iris-‐tes3ng.arff 25 25 25 25
25 25 Classifiers :
1. Naïve Bayes 2. K-‐Nearest Neighbor Classifier
(lazy àiBk) 3. Ar3ficial Neural Network
(func3on àmul3layer perceptron) 4. Support Vector Machine
(func3on à SMO)
Akurasi terhadap tes3ng set ?
iris setosa iris versicolor iris virginica
![Page 22: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/22.jpg)
22
Apakah yang dimaksud “mengukur akurasi”
• Tes3ng set “iris-‐tes3ng.arff” dilengkapi dengan informasi actual class-‐nya. Misalnya instance no.1 adalah suatu bunga yang memiliki sepal length 5.0 cm, sepal width 3.0cm, petal length 1.6 cm, petal width 0.2 cm, dan jenis bunganya (class) “Iris setosa”
• Model classifica3on yang dibangun harus mampu menebak
dengan benar class tersebut.
![Page 23: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/23.jpg)
23
Berbagai cara pengukuran akurasi
• “Using training set” : memakai seluruh data sebagai training set, sekaligus tes3ng set. Akurasi akan sangat 3nggi, tetapi 3dak memberikan es3masi akurasi yang sebenarnya terhadap data yang lain (yang 3dak dipakai untuk training)
• Hold Out Method : Memakai sebagian data sebagai training set, dan sisanya sebagai tes3ng set. Metode yang lazim dipakai, asal jumlah sampel cukup banyak.
Ada 2 : supplied test set dan percentage split. Pilihlah “Supplied test set” : jika file training dan tes3ng tersedia secara terpisah. Pilihlah “Percentage split” jika hanya ada 1 file yang ingin dipisahkan ke training & tes3ng. Persentase di kolom adalah porsi yang dipakai sbg training set
![Page 24: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/24.jpg)
24
Berbagai cara pengukuran akurasi
• Cross Valida3on Method ( fold = 5 atau 10 ) : teknik es3masi akurasi yang dipakai, jika jumlah sampel terbatas. Salah satu bentuk khusus CV adalah Leave-‐one-‐out Cross Valida3on (LOOCV) : dipakai jka jumlah sampel sangat terbatas
![Page 25: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/25.jpg)
25
Ilustrasi Cross Validation (k=5)
1. Data terdiri dari 100 instances (samples), dibagi ke dalam 5 blok dengan jumlah sampel yang sama. Nama blok : A, B, C, D dan E, masing-‐masing terdiri dari 20 instances
2. Kualitas kombinasi parameter tertentu diuji dengan cara sbb. step 1: training memakai A,B,C,D tes3ng memakai E akurasi a step 2: training memakai A,B,C,E tes3ng memakai D akurasi b step 3: training memakai A,B, D,E tes3ng memakai C akurasi c step 4: training memakai A, C,D,E tes3ng memakai B akurasi d step 5: training memakai B,C,D,E tes3ng memakai A akurasi e
3. Rata-‐rata akurasi : (a+b+c+d+e)/5 mencerminkan kualitas parameter yang dipilih
4. Ubahlah parameter model, dan ulangi dari no.2 sampai dicapai akurasi yang diinginkan
![Page 26: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/26.jpg)
26
Kali ini memakai “Supplied test set”. Selanjutnya klik pada bagian yang Di dalam kotak untuk men-‐set nilai Parameter. Dalam hal ini, adalah Nilai “k” pada k-‐Nearest Neighbour Classifier (Nick name : IBK)
![Page 27: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/27.jpg)
27
Set-‐lah nilai “k”misalnya 3 dan klik OK. Untuk memahami parameter yang lain, kliklah buQon “More” & “Capabili3es”
![Page 28: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/28.jpg)
28
Klik buQon “Start” Hasil eksperimen : Correct classifica3on rate : 96% (benar 72 dari total 75 data pada tes3ng set)
Bagaimana cara membaca Confusion matrix ?
![Page 29: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/29.jpg)
• Baris pertama “25 0 0” menunjukkan bahwa ada (25+0+0) instances class Iris-setosa di dalam file iris-testing.arff dan semua benar diklasifikasikan sebagai Iris setosa
• Baris kedua “0 24 1” menunjukkan bahwa ada (0+24+1) instances class Iris-versicolor di dalam file iris-testing.arff dan 1 salah diklasifikasikan sebagai Iris-virginica
• Baris ketiga “0 2 24” menunjukkan bahwa ada (0+2+23) instances class Iris-virginica di dalam file iris-testing.arff dan 2 di antaranya salah diklasifikasikan sebagai Iris-versicolor
![Page 30: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/30.jpg)
Untuk mengetahui instance mana yang 3dak berhasil Diklasifikasikan klik “More Op3ons” dan check lah “Output predic3ons”. Klik “Start” untuk mengulangi eksperimen yang sama
![Page 31: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/31.jpg)
Inst# : nomer urut data pada file “iris-‐tes3ng.arff” actual : class yang sebenarnya predicted: class yang diprediksi Error: jika ada misclassifica3on, akan diberikan tanda “+”
dalam contoh ini, pada instance no.34, 59 & 60
![Page 32: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/32.jpg)
Merangkum hasil eksperimen
No. K Correct Classification Rate Iris setosa Iris versicolor Iris virginica Total
1 1 ? ? ? ? 2 3 100% 96% 92% 96% 3 5 5 7 9
• Tugas : lanjutkan eksperimen di atas untuk nilai k = 1, 3, 5, 7 dan 9 • Buatlah grafik yang menunjukkan akurasi yang dicapai untuk masing-‐masing
class pada berbagai nilai k. Sumbu horisontal : nilai k dan sumbu ver3kal : akurasi
• Kapankah (pada nilai k berapa ?) akurasi ter3nggi dicapai ? Bagaimanakah trend akurasi masing-‐masing class ?
![Page 33: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/33.jpg)
33
Eksperimen memakai Neural Network
• Untuk eksperimen memakai neural network, caranya sama dengan k-‐Nearest Neighbor Classifier.
• Parameter yang dituning melipu3 antara lain: – hiddenLayers: banyaknya neuron
pada hidden layer. Default “a” : rata-‐rata jumlah neuron pada input & output layer
– LearningRate : biasanya nilai kecil (0.1, 0.01, 0.2, 0.3 dsb)
– Momentum: biasanya nilai besar (0.6, 0.9 dsb)
– trainingTime: maksimum iterasi backpropaga3on (500, 1000, 5000, 10000 dsb.)
![Page 34: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/34.jpg)
34
Eksperimen memakai SVM
![Page 35: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/35.jpg)
35
Eksperimen memakai SVM
C: complexity parameter (biasanya mengambil nilai besar. 100, 1000 dst)
Untuk memilih kernel
![Page 36: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/36.jpg)
Eksperimen memakai SVM
![Page 37: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/37.jpg)
37
Classification of cancers based on gene expression
• Biological reference: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks, J. Khan, et al., Nature Medicine 7, pp.673-679, 2001 (http://www.thep.lu.se/~carsten/pubs/lu_tp_01_06.pdf )
• Data is available from http://research.nhgri.nih.gov/microarray/Supplement/
• Small Round Blue Cell Tumors (SRBCT) has two class: – Ewing Family of Tumors (EWS) – NB: Neuroblastoma – BL: Burkitt lymphomas – RMS: Rhabdomyosarcoma : RMS
• Characteristic of the data – Training samples : 63 (EWS:23 BL:8 NB:12 RMS:20) – Testing samples: 20 (EWS:6 BL:3 NB:6 RMS:5) – Number of features (attributes): 2308
![Page 38: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/38.jpg)
38
Experiment using k-Nearest Neighbor Classifier • Training and testing set are given as separated arff file • Use training set to build a classifier: k-Nearest Neighbor (k=1) • Evaluate its performance on the testing set. • Change the value of k into 3,5,7 and 9 and repeat step 1 to 3 for each
value. Experiment using Artificial Neural Network • Do the same experiment using Multilayer Perceptron Artificial Neural
Network for various parameter setting (hidden neurons, learning rate, momentum, maximum iteration). Make at least five parameter settings.
Classification of cancers based on gene expression
![Page 39: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/39.jpg)
39
Parkinson Disease Detection
Max Little (Oxford University) recorded speech signals and measured the biomedical voice from 31 people, 23 with Parkinson Disease (PD). In the dataset which will be distributed during final examination, each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to "status" column which is set to 0 for healthy and 1 for PD. There are around six recordings per patient, making a total of 195 instances. (Ref. 'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23, 26 June 2007).
Experiment using k-Nearest Neighbor Classifier Conduct classification experiments using k-Nearest Neighbor Classifier and Support Vector Machines, by using 50% of the data as training set and the rest as testing set. Try at least 5 different values of k for k-Nearest neighbor, and draw a graph show the relationship between k and classification rate. In case of Support Vector Machine experiments, try several parameter combinations by modifying the type of Kernel and its parameters (at least 5 experiments). Compare and discuss the results obtained by both classifiers. Which of them achieved higher accuracy ?
![Page 40: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/40.jpg)
40
Parkinson Disease Detection
Max Little (Oxford University) recorded speech signals and measured the biomedical voice from 31 people, 23 with Parkinson Disease (PD). In the dataset which will be distributed during final examination, each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to "status" column which is set to 0 for healthy and 1 for PD. There are around six recordings per patient, making a total of 195 instances. (Ref. 'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23, 26 June 2007).
Experiment using k-Nearest Neighbor Classifier Conduct classification experiments using k-Nearest Neighbor Classifier and Support Vector Machines, by using 50% of the data as training set and the rest as testing set. Try at least 5 different values of k for k-Nearest neighbor, and draw a graph show the relationship between k and classification rate. In case of Support Vector Machine experiments, try several parameter combinations by modifying the type of Kernel and its parameters (at least 5 experiments). Compare and discuss the results obtained by both classifiers. Which of them achieved higher accuracy ?
![Page 41: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/41.jpg)
41
Practicing WEKA
• What is WEKA ? • Formatting the data into ARFF • Klasifikasi
– Tahapan membangun classifier – Contoh kasus : Klasifikasi bunga iris – Tahapan membangun classifier – Merangkum hasil eksperimen k-Nearest Neighbor Classifier – Eksperimen memakai classifier yang lain (JST, SVM) – Classification of cancers based on gene expression – Parkinson Disease Detection
• K-Means Clustering
![Page 42: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/42.jpg)
42
K-Means Clustering : Step by Step
• Pilihlah k buah data sebagai ini3al centroid • Ulangi
– Bentuklah K buah cluster dengan meng-‐assign 3ap data ke centroid terdekat
– Update-‐lah centroid 3ap cluster • Sampai centroid 3dak berubah
![Page 43: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/43.jpg)
43
K-Means Clustering : Step by Step
![Page 44: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/44.jpg)
Filename : kmeans_clustering.arff
![Page 45: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/45.jpg)
45
1 2
![Page 46: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/46.jpg)
46
Klik untuk memilih algoritma clustering
Pilih “Use training set”
![Page 47: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/47.jpg)
47
![Page 48: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/48.jpg)
48
Klik untuk memilih nilai k
![Page 49: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/49.jpg)
49
maxItera3ons: untuk menghen3kan proses clustering jika iterasi melebih nilai tertentu
numClusters: nilai k (banyaknya
cluster)
![Page 50: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/50.jpg)
50
Hasil clustering: terbentuk 3 cluster dan masing-‐masing beranggotakan 50 instances
![Page 51: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/51.jpg)
51
Klik dengan buQon kanan mouse untuk menampilkan visualisasi cluster
![Page 52: Cara pemakaian weka](https://reader033.vdocuments.site/reader033/viewer/2022042522/55b54326bb61eb3a3a8b4698/html5/thumbnails/52.jpg)
52
Nilai aQribute x ditampilkan pada sumbu x, dan nilai aQribute y ditampilkan pada sumbu y
Tiap cluster diberikan warna yang berbeda (merah, biru, hijau)