MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl
Proceedings of the First Workshop on Applications of Pattern Analysis, PMLR 11:44-50, 2010.

Abstract

Massive 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v11-bifet10a, title = {MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering}, author = {Bifet, Albert and Holmes, Geoff and Pfahringer, Bernhard and Kranen, Philipp and Kremer, Hardy and Jansen, Timm and Seidl, Thomas}, booktitle = {Proceedings of the First Workshop on Applications of Pattern Analysis}, pages = {44--50}, year = {2010}, editor = {Diethe, Tom and Cristianini, Nello and Shawe-Taylor, John}, volume = {11}, series = {Proceedings of Machine Learning Research}, address = {Cumberland Lodge, Windsor, UK}, month = {01--03 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v11/bifet10a/bifet10a.pdf}, url = {https://proceedings.mlr.press/v11/bifet10a.html}, abstract = {Massive 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.} }
Endnote
%0 Conference Paper %T MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering %A Albert Bifet %A Geoff Holmes %A Bernhard Pfahringer %A Philipp Kranen %A Hardy Kremer %A Timm Jansen %A Thomas Seidl %B Proceedings of the First Workshop on Applications of Pattern Analysis %C Proceedings of Machine Learning Research %D 2010 %E Tom Diethe %E Nello Cristianini %E John Shawe-Taylor %F pmlr-v11-bifet10a %I PMLR %P 44--50 %U https://proceedings.mlr.press/v11/bifet10a.html %V 11 %X Massive 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.
RIS
TY - CPAPER TI - MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering AU - Albert Bifet AU - Geoff Holmes AU - Bernhard Pfahringer AU - Philipp Kranen AU - Hardy Kremer AU - Timm Jansen AU - Thomas Seidl BT - Proceedings of the First Workshop on Applications of Pattern Analysis DA - 2010/09/30 ED - Tom Diethe ED - Nello Cristianini ED - John Shawe-Taylor ID - pmlr-v11-bifet10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 11 SP - 44 EP - 50 L1 - http://proceedings.mlr.press/v11/bifet10a/bifet10a.pdf UR - https://proceedings.mlr.press/v11/bifet10a.html AB - Massive 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. ER -
APA
Bifet, A., Holmes, G., Pfahringer, B., Kranen, P., Kremer, H., Jansen, T. & Seidl, T.. (2010). MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering. Proceedings of the First Workshop on Applications of Pattern Analysis, in Proceedings of Machine Learning Research 11:44-50 Available from https://proceedings.mlr.press/v11/bifet10a.html.

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