an introduction to weka
TRANSCRIPT
An Introduction to
WEKAECLT 5810/ SEEM 5750
The Chinese University of Hong Kong
Content
What is WEKA?
The Explorer:
Preprocess data
Classification
Clustering
Association Rules
Attribute Selection
Data Visualization
References and Resources
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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.
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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
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Main GUI
Three graphical user interfaces
“The Explorer” (exploratory data analysis)
“The Experimenter” (experimental environment)
“The KnowledgeFlow” (new process model inspired interface)
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Content
What is WEKA?
The Explorer:
Preprocess data
Classification
Clustering
Association Rules
Attribute Selection
Data Visualization
References and Resources
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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, …
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ARFF Format
@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
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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, …
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Explorer: 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:
milk, butter bread, eggs (with confidence 0.9 and
support 2000)
Apriori can compute all rules that have a given
minimum support and exceed a given confidence
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Explorer: attribute selection
Panel that can be used to investigate which (subsets of)
attributes are the most predictive ones
Attribute selection methods contain two parts:
A search method: best-first, forward selection, random,
exhaustive, genetic algorithm, ranking
An evaluation method: correlation-based, wrapper,
information gain, chi-squared, …
Very flexible: WEKA allows (almost) arbitrary
combinations of these two
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Explorer: data visualization
Visualization very useful in practice: e.g. helps to
determine difficulty of the learning problem
WEKA can visualize single attributes (1-d) and pairs of
attributes (2-d)
To do: rotating 3-d visualizations (Xgobi-style)
Color-coded class values
“Jitter” option to deal with nominal attributes (and to
detect “hidden” data points)
“Zoom-in” function
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References and Resources
References:
WEKA website: http://www.cs.waikato.ac.nz/~ml/weka/index.html
WEKA Tutorial: Machine Learning with WEKA: A presentation demonstrating
all graphical user interfaces (GUI) in Weka.
WEKA Data Mining Book: Ian H. Witten and Eibe Frank, Data Mining: Practical
Machine Learning Tools and Techniques (Second Edition)
WEKA Wiki: http://weka.sourceforge.net/wiki/index.php/Main_Page
Others: Jiawei Han and Micheline Kamber, Data Mining:
Concepts and Techniques, 2nd ed.