An Introduction to WEKA

1. An Introduction to WEKA SEEM 4630 10-2012

2. Content • What is WEKA? • The Explorer: • Preprocess data • Classification • Clustering • Association Rules • Attribute Selection • Data Visualization • References and Resources

3. What is WEKA? • Waikato Environment for Knowledge Analysis • It’s a data mining/machine learning tool developed by Department of Computer Science, University of Waikato, New Zealand. • Weka is also a bird found only on the islands of New Zealand.

4. Download and Install WEKA • Website: http://www.cs.waikato.ac.nz/~ml/weka/index.html • Support multiple platforms (written in java): • Windows, Mac OS X and Linux • WEKA has been installed in the teaching labs in SEEM

5. Main Features • 49 data preprocessing tools • 76 classification/regression algorithms • 8 clustering algorithms • 3 algorithms for finding association rules • 15 attribute/subset evaluators + 10 search algorithms for feature selection

6. Main GUI • Three graphical user interfaces • “The Explorer” (exploratory data analysis) • “The Experimenter” (experimental environment) • “The KnowledgeFlow” (new process model inspired interface)

7. Content • What is WEKA? • The Explorer: • Preprocess data • Classification • Clustering • Association Rules • Attribute Selection • Data Visualization • References and Resources

8. Explorer: pre-processing the data • Data can be imported from a file in various formats: ARFF, CSV, C4.5, binary • Data can also be read from a URL or from an SQL database (using JDBC) • Pre-processing tools in WEKA are called “filters” • WEKA contains filters for: • Discretization, normalization, resampling, attribute selection, transforming and combining attributes, …

9. @relation heart-disease-simplified @attribute age numeric @attribute sex { female, male} @attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina} @attribute cholesterol numeric @attribute exercise_induced_angina { no, yes} @attribute class { present, not_present} @data 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present ... WEKA only deals with “flat” files Flat file in ARFF (Attribute-Relation File Format) format 8/29/2014 9

10. WEKA only deals with “flat” files @relation heart-disease-simplified @attribute age numeric @attribute sex { female, male} @attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina} @attribute cholesterol numeric @attribute exercise_induced_angina { no, yes} @attribute class { present, not_present} @data 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present ... numeric attribute nominal attribute

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32. Explorer: building “classifiers” • Classifiers in WEKA are models for predicting nominal or numeric quantities • Implemented learning schemes include: • Decision trees and lists, instance-based classifiers, support vector machines, multi-layer perceptrons, logistic regression, Bayes’ nets, …

33. Decision Tree Induction: Training Dataset This follows an example of Quinlan’s ID3 (Playing Tennis)

34. age? <=30 overcast >40 31..40 student? credit rating? yes excellent fair no yes no yes no yes Output: A Decision Tree for “buys_computer”

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