Weka 3: Data Mining Software in Java
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Weka is open source software issued under the GNU General Public License.
The Weka mailing list
Please post Weka-related questions, comments, and bug reports to the Weka mailing list. There is also the searchable mailing list archive. Please do not email individual members of our research group about Weka problems.
Take a tour of Weka
RequirementsJava 1.4 is required to run Weka. Depending on your computing platform you may have to download and install it separately. It is available for free from Sun.
Downloading and installing WekaThere are different options for downloading and installing Weka 3.4 on your system:
Older versions of WekaClick here to download a jar archive containing Weka 3.0, the command-line-based version of Weka described in Chapter 8 of the data mining book by Ian H. Witten and Eibe Frank.
(weka-3-0-6.jar, 2,061,642 bytes).
All old versions of Weka are available from the Sourceforge website.
DocumentationWeka 3.4 has extensive help facilities built in. However, there is also:
Citing WekaIf you want to refer to Weka in a publication, please cite the data mining book. The full citation is "Data Mining: Practical machine learning tools with Java implementations," by Ian H. Witten and Eibe Frank, Morgan Kaufmann, San Francisco, 2000.
Collections of datasetsAvailable separately:
DevelopmentWe are following the Linux model of releases, where an even second digit of a release number indicates a "stable" release and an odd second digit indicates a "development" release (e.g. 3.0.x is a stable release, and 3.1.x is a developmental release). If you are using a developmental release, you might get to play with extra funky features, but it is entirely possible that these features come/go/transmogrify from one release to the next. If you require stability (e.g. if you are using Weka for teaching), use a stable release.
Weka via CVSIf you want to check out the current state of Weka as it is currently being worked on, you can do so via anonymous CVS. Set the CVSROOT environment variable to:
:pserver:firstname.lastname@example.org:/usr/local/global-cvs/ml_cvsThen you can checkout the latest snapshot of Weka using:
cvs login cvs co weka cvs logoutWhen you are asked for a password, just hit ENTER.
Contributors (not up to date)Abdelaziz Mahoui, Alexander K. Seewald, Ashraf M. Kibriya, Bernhard Pfahringer, Brent Martin, Eibe Frank, Gabi Schmidberger, Ian H. Witten, J. Lindgren, Janice Boughton, Jason Wells, Len Trigg, Lucio de Souza Coelho, Malcolm Ware, Mark Hall, Remco Bouckaert, Richard Kirkby, Shane Butler, Shane Legg, Stuart Inglis, Sylvain Roy, Tony Voyle, Xin Xu, Yong Wang, Zhihai Wang
Other Weka-related literatureWeka for kids.
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