a simple introduction to weka

A SIMPLE INTRODUCTION TO WEKA

Contents What is WEKA? WEKA Explorer Preprocessing the data Classification Clustering Association Rules Attribute Selection Data Visualization

Waikato Environment for Knowledge Analysis

Developed by Department of Computer Science, University of Waikato, New Zealand.

Weka is also a bird found only on theislands of New Zealand.

1 What is WEKA?

A collection of machine learning algorithmsfor data mining tasks.

Download and Install WEKA

Website:http://www.cs.waikato.ac.nz/~ml/weka/index.html

Platform independent

Exploratory data analysis

Experimental environment

New process model inspired interface

Command Line Interface

WEKA GUI

WEKA Explorer

2

Preprocessing the data

Classification

Clustering

Association Rules

Attribute Selection

Data Visualization

“Pre-Processing the data

Data can be imported from a file in various formats.ARFF-Attribute-Relation File FormatCSV - Comma Separated Values

Data can be read from a URL or from a SQL database.

Filters are used for pre-processing

@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}

@data63,male,typ_angina,233,no,not_present67,male,asympt,286,yes,present67,male,asympt,229,yes,present38,female,non_anginal,?,no,not_present...

WEKA only deals with flat files

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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 perceptron, logistic regression, Bayes’ nets, …

age income student credit_rating buys_computer<=30 high no fair no<=30 high no excellent no31…40 high no fair yes>40 medium no fair yes>40 low yes fair yes>40 low yes excellent no31…40 low yes excellent yes<=30 medium no fair no<=30 low yes fair yes>40 medium yes fair yes<=30 medium yes excellent yes31…40 medium no excellent yes31…40 high yes fair yes>40 medium no excellent no

Decision Tree Induction: Training Dataset

age?

overcast

student? credit rating?

<=30 >40

no yes yes

yes

31..40

nofairexcellentyesno

Output: A Decision Tree for “buys_computer”

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Clustering data Finding groups of similar instances in a

dataset

Implemented schemes in WEKA are:k-Means, EM, Cobweb, X-means, FarthestFirst

Finding Associations WEKA contains an implementation of the Apriori

algorithm for learning association rules

Works only with discrete data

Can identify statistical dependencies between groups of attributes:

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Used to determine the most predictive attributes

Consists of two parts

1.) A search method : best-first, forward selection, random, exhaustive, genetic algorithm and etc.

2.)An evaluation method : correlation-based, wrapper, information gain an etc.

Attribute Selection

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Data Visualization WEKA can visualize single attributes (1-d) and

pairs of attributes (2-d)

Color-coded class values

Use of “jitter” option

“Zoom-in” function

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The End