Weka is a popular open source machine learning tool developed at the University of Waikato. It contains algorithms for data preprocessing, classification, regression, clustering, association rules, and visualization. Weka supports various data formats and can be used through a graphical user interface, command line interface, or integrated into other Java code. It contains tools for exploring and analyzing data, applying machine learning algorithms, and evaluating experimental results.

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

15. Weka: An Introduction 15
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>]

16. Weka: An Introduction 16
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

18. Weka: An Introduction 18
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