1 Running Clustering Algorithm in Weka Presented by Rachsuda Jiamthapthaksin Computer Science Department University of Houston.

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Presentation transcript:

1 Running Clustering Algorithm in Weka Presented by Rachsuda Jiamthapthaksin Computer Science Department University of Houston

2 What is Weka? Data mining software in Java –Supervised learning (classification) –Unsupervised learning (clustering) Tools –Exploration –Visualization –Experiment –Statistical summary

3 Download Weka –Window (weka-3-5-6jre.exe) –Linux

4 Getting Start

5 Memory Limitation in Weka Run Chooser from DOS to increase memory C:\>java -Xmx128m -classpath.;/progra~1/weka-3-5/weka.jar weka.gui.GUIChooser

6 Weka GUI

7 Explorer

8 Open Files (.csv,.arff)

9 Dataset’s Description Attributes Dataset’s statistics

10 Remove Class Attribute Non-class attributes

11 Select A Clustering Algorithm

12 Select A Clustering Algorithm

13 Select A Clustering Algorithm

14 Parameters’ Setting

15 Run A Clustering Algorithm

16 DBSCAN Results === Run information === Scheme: weka.clusterers.DBScan -E 0.9 -M 6 -I weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase -D weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject Relation: iris-weka.filters.unsupervised.attribute.Remove-R5 Instances: 150 Attributes: 4 sepallength sepalwidth petallength petalwidth Test mode: evaluate on training data === Model and evaluation on training set === DBScan clustering results ======================================================================================== Clustered DataObjects: 150 Number of attributes: 4 Epsilon: 0.9; minPoints: 6 Index: weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase Distance-type: weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject Number of generated clusters: 1 Elapsed time:.06 ( 0.) 5.1,3.5,1.4,0.2 --> 0 ( 1.) 4.9,3,1.4,0.2 --> 0 ( 2.) 4.7,3.2,1.3,0.2 --> 0 ( 3.) 4.6,3.1,1.5,0.2 --> 0 ( 4.) 5,3.6,1.4,0.2 --> 0 … (146.) 6.3,2.5,5,1.9 --> 0 (147.) 6.5,3,5.2,2 --> 0 (148.) 6.2,3.4,5.4,2.3 --> 0 (149.) 5.9,3,5.1,1.8 --> 0 Clustered Instances (100%)

17 Simplify A Tested Dataset

18 Simplify A Tested Dataset

19 Parameters’ Setting

20 DBSCAN Clustering Results === Run information === Scheme: weka.clusterers.DBScan -E 0.3 -M 50 -I weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase -D weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject Relation: iris-weka.filters.unsupervised.attribute.Remove-R1-2,5 Instances: 150 Attributes: 2 petallength petalwidth Test mode: evaluate on training data === Model and evaluation on training set === DBScan clustering results ======================================================================================== Clustered DataObjects: 150 Number of attributes: 2 Epsilon: 0.3; minPoints: 50 Index: weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase Distance-type: weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject Number of generated clusters: 2 Elapsed time:.03 ( 0.) 1.4,0.2 --> 0 ( 1.) 1.4,0.2 --> 0 ( 2.) 1.3,0.2 --> 0 ( 3.) 1.5,0.2 --> 0 … (146.) 5,1.9 --> 1 (147.) 5.2,2 --> 1 (148.) 5.4,2.3 --> 1 (149.) 5.1,1.8 --> 1 Clustered Instances 0 50 ( 33%) ( 67%)

21 Run k-Means in Weka

22 Parameters’ Setting

23 k-Means Clustering Results === Run information === Scheme: weka.clusterers.SimpleKMeans -N 2 -S 10 Relation: iris-weka.filters.unsupervised.attribute.Remove-R1-2,5 Instances: 150 Attributes: 2 petallength petalwidth Test mode: evaluate on training data === Model and evaluation on training set === kMeans ====== Number of iterations: 6 Within cluster sum of squared errors: Cluster centroids: Cluster 0 Mean/Mode: Std Devs: Cluster 1 Mean/Mode: Std Devs: Clustered Instances ( 67%) 1 50 ( 33%)

24 ArffViewer: Convert Dataset’s Extension

25 Open A Dataset’s file

26 Select A Dataset’s File

27 View the Dataset

28 Manipulate the Dataset (Optional)

29 Save As.Arff File

30 Weka Documentation