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dc.contributor.authorBifet, Albert
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorKranen, Philipp
dc.contributor.authorKremer, Hardy
dc.contributor.authorJansen, Timm
dc.contributor.authorSeidl, Thomas
dc.coverage.spatialConference held at Windsor, UKen_NZ
dc.date.accessioned2011-01-14T03:14:22Z
dc.date.available2011-01-14T03:14:22Z
dc.date.issued2010
dc.identifier.citationBifet, A., Holmes, GeLee, W. G., Meurk, C. D. & Clarkson, B. D. (2008). MOA: Massive Online Analysis, a framework for stream classification and clustering. JMLR Workshop and Conference Proceedings Volume 11: Workshop on Applications of Pattern Analysis, 44-50.en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/4934
dc.description.abstractMassive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal with the challenging problem of scaling up the implementation of state of the art algorithms to real world dataset sizes. It contains collection of offline and online for both classification and clustering as well as tools for evaluation. In particular, for classification it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. For clustering, it implements StreamKM++, CluStream, ClusTree, Den-Stream, D-Stream and CobWeb. Researchers benefit from MOA by getting insights into workings and problems of different approaches, practitioners can easily apply and compare several algorithms to real world data set and settings. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherJMLRen_NZ
dc.relation.urihttp://jmlr.csail.mit.edu/proceedings/papers/v11/bifet10a/bifet10a.pdfen_NZ
dc.rights© 2010 A. Bifet, G. Holmes, B. Pfahringer, P. Kranen, H. Kremer, T. Jansen & T. Seidl.
dc.subjectcomputer scienceen_NZ
dc.subjectMassive Online Analysisen_NZ
dc.subjectMOAen_NZ
dc.titleMOA: Massive Online Analysis, a framework for stream classification and clustering.en_NZ
dc.typeConference Contributionen_NZ
dc.relation.isPartOfProceedings of the 1st Workshop on Applications of Pattern Analysisen_NZ
pubs.begin-page44en_NZ
pubs.elements-id20198
pubs.end-page50en_NZ
pubs.finish-date2010-09-03en_NZ
pubs.start-date2010-09-01en_NZ
pubs.volumeJMLR: Workshop and Conference Proceedings 11en_NZ


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