Waikato Environment for Knowledge Analysis

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Weka (software) logo.png
A screenshot from Weka 3.5.5.
Screenshot from Weka
Basic data

developer University of Waikato
Current  version 3.8.3
( December 22, 2017 )
operating system Platform independent
programming language Java
category Machine learning
License GPL , proprietary
German speaking No

Weka (Waikato Environment for Knowledge Analysis) is software that provides various techniques from the areas of machine learning and data mining . The program was developed at the University of Waikato and is written in Java . It is a freely available software that is under the GNU General Public License .

The software is an integral part of the book Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten, Eibe Frank and Mark A. Hall, the standard English-language work on machine learning. The software was recognized by the Association for Computing Machinery in 2005 with the "SIGKDD Service Award" for its high contribution to research, among other things by providing the source texts as open source .

Weka is known for its multitude of classifiers such as Bayesian classifiers , artificial neural networks , support vector machines , decision trees , ID3 -, C4.5 - but also meta-classifiers, boosting and ensembles. In other data mining areas such as cluster analysis , only the most basic methods such as the k-means algorithm and the EM algorithm are offered.


The WEKA workbench is divided into the following areas:

See also

  • ELKI - complementary software with a focus on cluster analysis methods, outlier detection and index structures
  • KNIME (Konstanz Information Miner) project of the University of Konstanz for interactive data analysis in Eclipse.
  • RapidMiner - can use Weka algorithms.
  • Scikit-learn a free machine learning software library for the Python programming language

Individual evidence

  1. www.cs.waikato.ac.nz . (accessed on September 24, 2019).
  2. ^ Ian H. Witten, Eibe Frank, Mark A. Hall : Data Mining: Practical Machine Learning Tools and Techniques . 3. Edition. Morgan Kaufmann, Burlington MA 2011, ISBN 978-0-12-374856-0 ( cs.waikato.ac.nz ).
  3. SIGKDD Service Awards . Association for Computing Machinery , accessed January 21, 2016 .
  4. KDNuggets News 2005-13. KDnuggets, accessed April 15, 2011 .