Running Clustering Algorithm in Weka

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Running Clustering Algorithm in Weka
Presented by Rachsuda Jiamthapthaksin Computer
Science Department University 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\gt java -Xmx128m -classpath ./progra1/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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Datasets Description
Datasets statistics
Attributes
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Remove Class Attribute
Non-class attributes
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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.Se
  quentialDatabase -D weka.clusterers.forOPTICSAndDB
  Scan.DataObjects.EuclidianDataObject
• Relation iris-weka.filters.unsupervised.attri
  bute.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

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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.Se
  quentialDatabase -D weka.clusterers.forOPTICSAndDB
  Scan.DataObjects.EuclidianDataObject
• Relation iris-weka.filters.unsupervised.attri
  bute.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.Database
  s.SequentialDatabase

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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 10
• Relation iris-weka.filters.unsupervised.attri
  bute.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
  5.179687509974782

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ArffViewer Convert Datasets Extension
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Open A Datasets file
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Select A Datasets 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