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Running Clustering Algorithm in Weka

Presented by Rachsuda Jiamthapthaksin

Computer Science DepartmentUniversity of Houston

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What is Weka?

• Data mining software in Java– Supervised learning (classification)– Unsupervised learning (clustering)

• Tools– Exploration– Visualization– Experiment– Statistical summary

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Download Weka

• http://www.cs.waikato.ac.nz/ml/weka/– Window (weka-3-5-6jre.exe)– Linux

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Getting Start

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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

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Weka GUI

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Explorer

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Open Files (.csv, .arff)

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Dataset’s Description

Attributes

Dataset’sstatistics

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Remove Class Attribute

Non-classattributes

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Select A Clustering Algorithm

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Select A Clustering Algorithm

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Select A Clustering Algorithm

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Parameters’ Setting

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Run A Clustering Algorithm

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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-R5Instances: 150Attributes: 4 sepallength sepalwidth petallength petalwidthTest mode: evaluate on training data

=== Model and evaluation on training set ===

DBScan clustering results========================================================================================

Clustered DataObjects: 150Number of attributes: 4Epsilon: 0.9; minPoints: 6Index: weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabaseDistance-type: weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObjectNumber of generated clusters: 1Elapsed 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

0 150 (100%)

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Simplify A Tested Dataset

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Simplify A Tested Dataset

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Parameters’ Setting

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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-R1-2,5Instances: 150Attributes: 2 petallength petalwidthTest mode: evaluate on training data

=== Model and evaluation on training set ===

DBScan clustering results========================================================================================

Clustered DataObjects: 150Number of attributes: 2Epsilon: 0.3; minPoints: 50Index: weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabaseDistance-type: weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObjectNumber of generated clusters: 2Elapsed 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%)1 100 ( 67%)

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Run k-Means in Weka

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Parameters’ Setting

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k-Means Clustering Results=== Run information ===

Scheme: weka.clusterers.SimpleKMeans -N 2 -S 10Relation: iris-weka.filters.unsupervised.attribute.Remove-R1-2,5Instances: 150Attributes: 2 petallength petalwidthTest mode: evaluate on training data

=== Model and evaluation on training set ===

kMeans======

Number of iterations: 6Within cluster sum of squared errors: 5.179687509974782

Cluster centroids:

Cluster 0Mean/Mode: 4.906 1.676 Std Devs: 0.8256 0.4248

Cluster 1Mean/Mode: 1.464 0.244 Std Devs: 0.1735 0.1072

Clustered Instances

0 100 ( 67%)1 50 ( 33%)

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ArffViewer: Convert Dataset’s Extension

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Open A Dataset’s file

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Select A Dataset’s File

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View the Dataset

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Manipulate the Dataset (Optional)

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Save As .Arff File

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Weka Documentation