MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering. Bifet, A., Holmes, G., Pfahringer, B., Kranen, P., Kremer, H., Jansen, T., & Seidl, T. In Proceedings of the First Workshop on Applications of Pattern Analysis, pages 44–50, September, 2010. PMLR. ISSN: 1938-7228![MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 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.
@inproceedings{bifet_moa_2010,
title = {{MOA}: {Massive} {Online} {Analysis}, a {Framework} for {Stream} {Classification} and {Clustering}},
shorttitle = {{MOA}},
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.},
language = {en},
urldate = {2022-03-15},
booktitle = {Proceedings of the {First} {Workshop} on {Applications} of {Pattern} {Analysis}},
publisher = {PMLR},
author = {Bifet, Albert and Holmes, Geoff and Pfahringer, Bernhard and Kranen, Philipp and Kremer, Hardy and Jansen, Timm and Seidl, Thomas},
month = sep,
year = {2010},
note = {ISSN: 1938-7228},
pages = {44--50},
}
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