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Contributed by Yizhou Sun 2008 An Introduction to WEKA
Content ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],04/26/10
What is WEKA? ,[object Object],[object Object],[object Object],04/26/10
Download and Install WEKA ,[object Object],[object Object],[object Object],04/26/10
Main Features ,[object Object],[object Object],[object Object],[object Object],[object Object],04/26/10
Main GUI ,[object Object],[object Object],[object Object],[object Object],04/26/10
Content ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],04/26/10
Explorer: pre-processing the data ,[object Object],[object Object],[object Object],[object Object],[object Object],04/26/10
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],WEKA only deals with “flat” files 04/26/10 Flat file in ARFF format
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],WEKA only deals with “flat” files 04/26/10 numeric attribute nominal attribute
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
Explorer: building “classifiers” ,[object Object],[object Object],[object Object],04/26/10
April 26, 2010 This follows an example of Quinlan’s ID3 (Playing Tennis) Decision Tree Induction: Training Dataset
April 26, 2010 Output: A Decision Tree for “buys_computer” age? overcast student? credit rating? <=30 >40 no yes yes yes 31..40 no fair excellent yes no
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],April 26, 2010 Algorithm for Decision Tree Induction
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
Explorer: clustering data ,[object Object],[object Object],[object Object],[object Object],[object Object],04/26/10
[object Object],[object Object],[object Object],[object Object],[object Object],April 26, 2010 The K-Means Clustering Method
[object Object]
Explorer: finding associations ,[object Object],[object Object],[object Object],[object Object],[object Object],04/26/10
Basic Concepts: Frequent Patterns ,[object Object],[object Object],[object Object],[object Object],[object Object],April 26, 2010 Customer buys diaper Customer buys both Customer buys beer Tid Items bought 10 Beer, Nuts, Diaper 20 Beer, Coffee, Diaper 30 Beer, Diaper, Eggs 40 Nuts, Eggs, Milk 50 Nuts, Coffee, Diaper, Eggs, Milk
Basic Concepts: Association Rules ,[object Object],[object Object],[object Object],[object Object],[object Object],April 26, 2010 Customer buys diaper Customer buys both Customer buys beer Nuts, Eggs, Milk 40 Nuts, Coffee, Diaper, Eggs, Milk 50 Beer, Diaper, Eggs 30 Beer, Coffee, Diaper 20 Beer, Nuts, Diaper 10 Items bought Tid ,[object Object],[object Object],[object Object]
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
Explorer: attribute selection ,[object Object],[object Object],[object Object],[object Object],[object Object],04/26/10
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
Explorer: data visualization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],04/26/10
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
04/26/10 University of Waikato
References and Resources ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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WEKA Introduction Data Mining Tool