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dc.contributor.authorFrank, Eibe
dc.contributor.authorHall, Mark A.
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorKirkby, Richard Brendon
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorWitten, Ian H.
dc.date.accessioned2008-11-30T20:42:41Z
dc.date.available2008-11-30T20:42:41Z
dc.date.issued2005
dc.identifier.citationFrank, E., Hall, M.A., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I.H.(2005). Weka: A machine learning workbench for data mining. In O. Maimon & L. Rokach(Eds), Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers(pp. 1305-1314). Berlin: Springer.en_US
dc.identifier.isbn978-0-387-24435-8
dc.identifier.urihttps://hdl.handle.net/10289/1497
dc.description.abstractThe Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data preprocessing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up large-scale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced environment for experimental data mining. The system is written in Java and distributed under the terms of the GNU General Public License.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringeren_US
dc.rightsThe article has been published in the book: Data Mining and Knowledge Discovery Handbook. © Springer, Berlin. Used with permission. The book is available at: http://www.springer.com/978-0-387-24435-8en_US
dc.subjectcomputer scienceen_US
dc.subjectmachine learning softwareen_US
dc.subjectdata miningen_US
dc.subjectdata preprocessingen_US
dc.subjectdata visulalizationen_US
dc.subjectextensible workbenchen_US
dc.subjectMachine learning
dc.titleWeka: A machine learning workbench for data miningen_US
dc.typeChapter in Booken_US
dc.identifier.doi10.1007/0-387-25465-X_62en_NZ
dc.relation.isPartOfData Mining and Knowledge Discovery Handbooken_NZ
pubs.begin-page1305en_NZ
pubs.elements-id8350
pubs.end-page1314en_NZ
uow.identifier.chapter-no62en_NZ


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