Weka (software)
Developer(s) | University of Waikato |
---|---|
Stable release | 3.8.6 (stable)
/ January 28, 2022 |
Preview release | 3.9.6
/ January 28, 2022 |
Repository | |
Written in | Java SE |
Type | Machine learning |
License | GNU General Public License |
Website | www |
Waikato Environment for Knowledge Analysis (Weka) is a collection of machine learning and data analysis free software licensed under the GNU General Public License. It was developed at the University of Waikato, New Zealand and is the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques".[1]
Description
Weka contains a collection of visualization tools and algorithms for
- Free availability under the GNU General Public License.
- Portability, since it is fully implemented in the Java programming languageand thus runs on almost any modern computing platform.
- A comprehensive collection of data preprocessing and modeling techniques.
- Ease of use due to its graphical user interfaces.
Weka supports several standard
Extension packages
In version 3.7.2, a package manager was added to allow the easier installation of extension packages.[6] Some functionality that used to be included with Weka prior to this version has since been moved into such extension packages, but this change also makes it easier for others to contribute extensions to Weka and to maintain the software, as this modular architecture allows independent updates of the Weka core and individual extensions.
History
- In 1993, the University of Waikato in New Zealand began development of the original version of Weka, which became a mix of Tcl/Tk, C, and makefiles.
- In 1997, the decision was made to redevelop Weka from scratch in Java, including implementations of modeling algorithms.[7]
- In 2005, Weka received the
- In 2006, Pentaho Corporation acquired an exclusive licence to use Weka for business intelligence.[10] It forms the data mining and predictive analytics component of the Pentaho business intelligence suite. Pentaho has since been acquired by Hitachi Vantara, and Weka now underpins the PMI (Plugin for Machine Intelligence) open source component.[11]
Related tools
- Auto-WEKA is an automated machine learning system for Weka.[12]
- Environment for DeveLoping KDD-Applications Supported by Index-Structures (ELKI) is a similar project to Weka with a focus on cluster analysis, i.e., unsupervised methods.
- H2O.ai is an open-source data science and machine learning platform
- KNIME is a machine learning and data mining software implemented in Java.
- Massive Online Analysis (MOA) is an open-source project for large scale mining of data streams, also developed at the University of Waikato in New Zealand.
- Neural Designer is a data mining software based on deep learning techniques written in C++.
- Orange is a similar open-source project for data mining, machine learning and visualization based on scikit-learn.
- RapidMiner is a commercial machine learning framework implemented in Java which integrates Weka.
- scikit-learn is a popular machine learning library in Python.
See also
References
- ^ ISBN 9780080890364. Retrieved 2011-01-19.
- ^ Holmes, Geoffrey; Donkin, Andrew; Witten, Ian H. (1994). Weka: A machine learning workbench (PDF). Proceedings of the Second Australia and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia. Retrieved 2007-06-25.
- ^ Garner, Stephen R.; Cunningham, Sally Jo; Holmes, Geoffrey; Nevill-Manning, Craig G.; Witten, Ian H. (1995). Applying a machine learning workbench: Experience with agricultural databases (PDF). Proceedings of the Machine Learning in Practice Workshop, Machine Learning Conference, Tahoe City (CA), USA. pp. 14–21. Retrieved 2007-06-25.
- ^ "Weka Package Metadata". 2017. Retrieved 2017-11-11 – via SourceForge.
- CiteSeerX 10.1.1.459.8443.
- ^ "weka-wiki - Packages". Retrieved 27 January 2020 – via GitHub.
- ^ Witten, Ian H.; Frank, Eibe; Trigg, Len; Hall, Mark A.; Holmes, Geoffrey; Cunningham, Sally Jo (1999). Weka: Practical Machine Learning Tools and Techniques with Java Implementations (PDF). Proceedings of the ICONIP/ANZIIS/ANNES'99 Workshop on Emerging Knowledge Engineering and Connectionist-Based Information Systems. pp. 192–196. Retrieved 2007-06-26.
- ^ Piatetsky-Shapiro, Gregory I. (2005-06-28). "Winner of SIGKDD Data Mining and Knowledge Discovery Service Award". KDnuggets. Retrieved 2007-06-25.
- ^ "Overview of SIGKDD Service Award winners". ACM. 2005. Retrieved 2007-06-25.
- ^ "Pentaho Acquires Weka Project". Pentaho. Retrieved 2018-02-06.
- Hitachi Vantara.
- ISBN 978-1-4503-2174-7.
External links
- Official website at University of Waikato in New Zealand