View source for Weka (software)

{{Short description|Suite of machine learning software written in Java}}
{{Redirect|WEKA||Weka (disambiguation)}}
{{Infobox software
| name = Weka
| logo = Weka (software) logo.png
| logo size = 198px
| logo caption = Weka logo, featuring [[weka]], a bird [[Endemism|endemic]] to New Zealand
| screenshot = Weka-3.5.5.png
| caption = Weka 3.5.5 Explorer window open with Iris [[University of California, Irvine|UCI]] dataset
| developer = [[University of Waikato]]
| latest release version = 3.8.6 (stable)
| latest release date = {{Start date and age|2022|01|28}}
| latest preview version = 3.9.6
| latest preview date = {{Start date and age|2022|01|28}}
| operating system = [[Windows]], [[macOS]], [[Linux]]
| platform = [[IA-32]], [[x86-64]], [[ARM_architecture]]; [[Java SE]]
| programming language = [[Java (programming language)|Java]]
| genre = [[Machine learning]]
| license = [[GNU General Public License]]
| website = {{URL|https://ml.cms.waikato.ac.nz/weka}}
}}
'''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".<ref name="Data Mining" />

==Description==
Weka contains a collection of visualization tools and algorithms for [[data analysis]] and [[predictive modeling]], together with graphical user interfaces for easy access to these functions.<ref name="Data Mining">{{Cite book |last1=Witten |first1=Ian H. |author-link1=Ian H. Witten |url=http://www.cs.waikato.ac.nz/~ml/weka/book.html |title=Data Mining: Practical machine learning tools and techniques |last2=Frank |first2=Eibe |last3=Hall |first3=Mark A. |last4=Pal |first4=Christopher J. |publisher=[[Morgan Kaufmann Publishers|Morgan Kaufmann]] |year=2011 |isbn=9780080890364 |edition=3rd |location=San Francisco (CA) |access-date=2011-01-19}}</ref> The original non-Java version of Weka was a [[Tcl]]/[[Tk (software)|Tk]] front-end to (mostly third-party) modeling algorithms implemented in other programming languages, plus [[data preprocessing]] utilities in [[C (programming language)|C]], and a [[makefile]]-based system for running machine learning experiments. This original version was primarily designed as a tool for analyzing data from agricultural domains,<ref>{{Cite conference |last1=Holmes |first1=Geoffrey |last2=Donkin |first2=Andrew |last3=Witten |first3=Ian H. |year=1994 |title=Weka: A machine learning workbench |url=https://www.cs.waikato.ac.nz/~ml/publications/1994/Holmes-ANZIIS-WEKA.pdf |conference=Proceedings of the Second Australia and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia |access-date=2007-06-25}}</ref><ref>{{Cite conference |last1=Garner |first1=Stephen R. |last2=Cunningham |first2=Sally Jo |last3=Holmes |first3=Geoffrey |last4=Nevill-Manning |first4=Craig G. |author-link4=Craig Nevill-Manning |last5=Witten |first5=Ian H. |year=1995 |title=Applying a machine learning workbench: Experience with agricultural databases |url=http://www.cs.waikato.ac.nz/~ml/publications/1995/Garner95-imlc95.pdf |conference=Proceedings of the Machine Learning in Practice Workshop, [[International Conference on Machine Learning|Machine Learning Conference]], Tahoe City (CA), USA |pages=14–21 |access-date=2007-06-25}}</ref> but the more recent fully [[Java (programming language)|Java]]-based version (Weka 3), for which development started in 1997, is now used in many different application areas, in particular for educational purposes and research. Advantages of Weka include:

* Free availability under the [[GNU General Public License]].
* Portability, since it is fully implemented in the [[Java programming language]] and 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 [[data mining]] tasks, more specifically, data preprocessing, [[data clustering|clustering]], [[Statistical classification|classification]], [[Regression analysis|regression]], [[data visualization|visualization]], and [[feature selection]]. Input to Weka is expected to be formatted according the Attribute-Relational File Format and with the filename bearing the {{Not a typo|.arff}} extension. All of Weka's techniques are predicated on the assumption that the data is available as one flat file or relation, where each data point is described by a fixed number of attributes (normally, numeric or nominal attributes, but some other attribute types are also supported). Weka provides access to [[SQL]] [[database]]s using [[Java Database Connectivity]] and can process the result returned by a database query. Weka provides access to [[deep learning]] with [[Deeplearning4j]].<ref>{{cite web |url=http://weka.sourceforge.net/packageMetaData/ |title=Weka Package Metadata |access-date=2017-11-11 |year=2017 |via=[[SourceForge]] }}</ref> It is not capable of multi-relational data mining, but there is separate software for converting a collection of linked database tables into a single table that is suitable for processing using Weka.<ref>{{cite journal |title=Proper: A Toolbox for Learning from Relational Data with Propositional and Multi-Instance Learners |first1=Peter |last1=Reutemann |first2=Bernhard |last2=Pfahringer |first3=Eibe |last3=Frank |year=2004 |journal=17th Australian Joint Conference on Artificial Intelligence (AI2004) |publisher=Springer-Verlag |citeseerx = 10.1.1.459.8443}}</ref> Another important area that is currently not covered by the algorithms included in the Weka distribution is sequence modeling.

== Extension packages ==
In version 3.7.2, a package manager was added to allow the easier installation of extension packages.<ref>{{cite web |url=https://waikato.github.io/weka-wiki/packages/ |title=weka-wiki - Packages |access-date=27 January 2020 |via=[[GitHub]]}}</ref>
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.<ref>{{Cite conference |last1=Witten |first1=Ian H. |last2=Frank |first2=Eibe |last3=Trigg |first3=Len |last4=Hall |first4=Mark A. |last5=Holmes |first5=Geoffrey |last6=Cunningham |first6=Sally Jo |year=1999 |title=Weka: Practical Machine Learning Tools and Techniques with Java Implementations |url=http://www.cs.waikato.ac.nz/~ml/publications/1999/99IHW-EF-LT-MH-GH-SJC-Tools-Java.pdf |conference=Proceedings of the ICONIP/ANZIIS/ANNES'99 Workshop on Emerging Knowledge Engineering and Connectionist-Based Information Systems |pages=192–196 |access-date=2007-06-26}}</ref>
* In 2005, Weka received the [[SIGKDD]] Data Mining and Knowledge Discovery Service Award.<ref>{{cite news |url=http://www.kdnuggets.com/news/2005/n13/2i.html |publisher=KDnuggets|title=Winner of SIGKDD Data Mining and Knowledge Discovery Service Award |access-date=2007-06-25 |first1=Gregory I. |last1=Piatetsky-Shapiro |author-link1=Gregory Piatetsky-Shapiro |date=2005-06-28 }}</ref><ref>{{cite web |url=http://www.acm.org/sigs/sigkdd/awards_service.php |title=Overview of SIGKDD Service Award winners |access-date=2007-06-25 |year=2005|publisher= ACM}}</ref>
* In 2006, [[Pentaho]] Corporation acquired an exclusive licence to use Weka for [[business intelligence]].<ref>{{Cite news |url=http://www.pentaho.com/pentaho-acquires-weka-project |title=Pentaho Acquires Weka Project |publisher=[[Pentaho]] |access-date=2018-02-06 |language=en }}</ref> 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.<ref>{{Cite web |url=https://community.hitachivantara.com/s/article/machine-intelligence-made-easy |title=Plugin for Machine Intelligence |publisher=[[Hitachi Vantara]]}}</ref>

==Related tools==
* [[Auto-WEKA]] is an [[automated machine learning]] system for Weka.<ref name="Auto-WEKA">{{Cite conference |last1=Thornton |first1=Chris |last2=Hutter |first2=Frank |last3=Hoos |first3=Holger H. |author-link3=Holger H. Hoos |last4=Leyton-Brown |first4=Kevin |author-link4=Kevin Leyton-Brown |date=2013-08-11 |title=Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms |conference=[[Special Interest Group on Knowledge Discovery and Data Mining|Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining]] |language=en |publisher=ACM |pages=847–855 |doi=10.1145/2487575.2487629 |isbn=978-1-4503-2174-7}}</ref>
* 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 (Software)|H2O.ai]] is an open-source data science and machine learning platform
* [[KNIME]] is a machine learning and data mining software implemented in [[Java (programming language)|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 (software)|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 (programming language)|Java]] which integrates Weka.
* [[scikit-learn]] is a popular machine learning library in Python.

==See also==
{{Portal|Free and open-source software}}
* [[List of numerical-analysis software]]

==References==
{{Reflist}}

==External links==
{{Commons category|Weka (machine learning)}}
* {{Official website|ml.cms.waikato.ac.nz/weka}} at University of Waikato in New Zealand

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{{Numerical analysis software}}

{{DEFAULTSORT:Weka (machine learning)}}
[[Category:Data mining and machine learning software]]
[[Category:Free artificial intelligence applications]]
[[Category:Free data analysis software]]
[[Category:Free science software]]
[[Category:Free software programmed in Java (programming language)]]
[[Category:Software using the GNU General Public License]]