Waikato/moa has 670 GitHub stars and 369 forks. GitHot measured 0 stars in 24 hours. Last checked 2026-11-09 03:28 UTC. Last push 2026-11-06.
MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
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README.md
OriginalMOA (Massive Online Analysis)
MOA is the most popular open source framework for data stream mining, with a very active growing community (blog). It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems.
Using MOA
MOA performs BIG DATA stream mining in real time, and large scale machine learning. MOA can be extended with new mining algorithms, and new stream generators or evaluation measures. The goal is to provide a benchmark suite for the stream mining community.
Mailing lists
- MOA users: http://groups.google.com/group/moa-users
- MOA developers: http://groups.google.com/group/moa-development
Citing MOA
If you want to refer to MOA in a publication, please cite the following JMLR paper:
Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010); MOA: Massive Online Analysis; Journal of Machine Learning Research 11: 1601-1604
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