WEKA AN INTRODUCTION

WEKA Waikato Environment for Knowledge Analysis

(WEKA) Developed by the Department of Computer Science,

University of Waikato, New Zealand Machine learning/data mining software written in

Java (distributed under the GNU Public License) Used for research, education, and applications http://www.cs.waikato.ac.nz/ml/weka/

Weka Interfaces Explorer

Preprocessing, attribute selection, learning, visualization

Knowledge Flow Visual design of KDD process

Experimenter testing and evaluating machine learning

algorithms Command-line

Data Formats Uses flat text files to describe the data Can work with a wide variety of data files including

its own .arff format and C4.5 file formats Data can be imported from a file in various formats:

ARFF, CSV, C4.5 etc. ARFF (Attribute Relation File Format)

@relation person

@attribute age numeric @attribute name string @attribute education {College, Masters, Doctorate} @attribute class {>50K,

Native format ARFF Supports file Conversions

Explorer Applying Filters: Supervised Vs Unsupervised Filters Attribute Vs Instance Filters Unsupervised Attribute Filters

Add-Adds a new attribute Normalize-Scales all numeric values Remove-Remove Attributes (RemoveType /

RemoveUseless) Unsupervised Instance Filters

Randomize- Randomize order of instance in a dataset

RemoveWithValues- Filter out instances with certain attribute values

Supervised Attribute Filters AttributeSelection- Attribute Selection Methods Discretize- Convert Numeric attributes to nominal

Supervised Instance Filters Resample- Produce a random sub sample of a

dataset

Classifiers: Bayes - BayesNet, NaiveBayes Trees - ID3, J48 Rules - OneR, Conjunctive Rule Functions - Linear Regression,

RBFNetwork, Multilayer Perceptron

Lazy - KStar, IBk Miscellaneous- VFI

Clusterers: OPTICS DBScan SimpleKMeans Cobweb

Associations: Apriori Predictive Apriori Filtered Associator

Attribute Selection: Attribute Evaluators

CfsSubsetEval ClassifierSubsetEval GainRatioAttributeEval InfoGainAttributeEval

Search Method Best First Exhaustive Search Genetic Search Rank Search

Knowledge Flow Interface: Data-flow inspired interface to WEKA process data in batches or incrementally process multiple batches or streams in parallel (each

separate flow executes in its own thread) chain filters together visualize performance of incremental classifiers

during processing

Experimenter Interface: Enables the user to create, run, modify, and analyse

experiments in a more convenient manner Modes of Operation

Simple Advanced

Local / Remote Experiments are supported

Command Line Interface: Plain text panel from where commands can be

entered java [] invokes a java class

with the given arguments (if any) break stops the current thread, e.g., a running

classifier, in a friendly manner kill stops the current thread in an unfriendly

fashion cls clears the output area exit exits the Simple CLI help []

Weka Operation: The Operating Systems command line interface can

also be used after setting the CLASSPATH accordingly.

All the functionality supported by Weka can also be invoked from ones own source code.

Weka Extensions: BioWeka - Extension library for knowledge

discovery in biology WekaMetal - Meta learning extension to WEKA

Weka-Parallel - Parallel processing for WEKA Grid Weka - Grid computing using WEKA

References: Witten, I.H. and Frank, E. (2005) Data Mining:

Practical machine learning tools and techniques. 2nd edition Morgan Kaufmann, San Francisco

Weka Knowledge Flow Tutorial, Mark Hall Peter Reutemann http://www.inf.fh-dortmund.de/personen/professoren/engels/dm/praktikum/WEKA-KnowledgeFlowTutorial-3-5-7.pdf

WEKA Manual for Version 3-6-2 - Remco R. Bouckaert, Eibe Frank et.al, January 11, 2010

WEKA AN INTRODUCTIONWEKA Waikato Environment for Knowledge Analysis (WEKA) Developed by the Department of Computer Science, University of Waikato, New Zealand Machine learning/data mining software written in Java (distributed under the GNU Public License) Used for research, education, and applications http://www.cs.waikato.ac.nz/ml/weka/

Weka Interfaces Explorer Preprocessing, attribute selection, learning, visualization

Knowledge Flow Visual design of KDD process

Experimenter testing and evaluating machine learning algorithms

Command-line

Data Formats Uses flat text files to describe the data Can work with a wide variety of data files including its own .arff format and C4.5 file formats Data can be imported from a file in various formats: ARFF, CSV, C4.5 etc. ARFF (Attribute Relation File Format)@relation person@attribute age numeric @attribute name string @attribute education {College, Masters, Doctorate} @attribute class {>50K,