Weka – An Introduction

1. Weka: An Introduction 1
Weka – An Introduction

2. Weka: An Introduction 2
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/

3. Weka: An Introduction 3
Weka Interfaces
 Explorer
 Preprocessing, attribute selection, learning, visualization
 Knowledge Flow
 Visual design of KDD process
 Experimenter
 testing and evaluating machine learning algorithms
 Command-line

4. Weka: An Introduction 4
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.

5. Weka: An Introduction 5
Data Formats (Contd.)
 ARFF (Attribute Relation File Format)
@relation person
@attribute age numeric
@attribute name string
@attribute education {College, Masters, Doctorate}
@attribute class {>50K,<=50K}
@data
 Supported Data types
 Numeric
 String
 Nominal
 Date
 Relational

6. Weka: An Introduction 6
Explorer
 Supports Exploratory Data Analysis
 Preprocess: Choose and modify the data being acted on.
 Classify: Train and test learning schemes that classify or
perform regression.
 Cluster: Learn clusters for the data.
 Associate: Learn association rules for the data.
 Select attributes: Select the most relevant attributes in the
data.
 Visualize: View an interactive 2D plot of the data.

7. Weka: An Introduction 7
Explorer - Preprocessing
 Loading Data
 Open file
 Open URL
 Open DB
 Generate
 Native format – ARFF
 Supports file Conversions

8. Weka: An Introduction 8
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

9. Weka: An Introduction 9
Classifiers
 Bayes - BayesNet, NaiveBayes
 Trees - ID3, J48
 Rules - OneR, Conjunctive Rule
 Functions - Linear Regression,
RBFNetwork,
Multilayer Perceptron
 Lazy - KStar, IBk
 Miscellaneous- VFI

10. Weka: An Introduction 10
Clusterers
 OPTICS
 DBScan
 SimpleKMeans
 Cobweb

11. Weka: An Introduction 11
Associations
 Apriori
 Predictive Apriori
 Filtered Associator

12. Weka: An Introduction 12
Attribute Selection
 Attribute Evaluators
 CfsSubsetEval
 ClassifierSubsetEval
 GainRatioAttributeEval
 InfoGainAttributeEval
 Search Method
 Best First
 Exhaustive Search
 Genetic Search
 Rank Search

13. Weka: An Introduction 13
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

14. Weka: An Introduction 14
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

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Command Line Interface
 Plain text panel from where commands can be
entered
 java <classname> [<args>] 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 [<command>]

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Weka Operation
 The Operating System’s command line interface can also be
used after setting the CLASSPATH accordingly.
 All the functionality supported by Weka can also be invoked
from one’s own source code.

17. Weka: An Introduction 17
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

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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