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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.

Home Page: http://moa.cms.waikato.ac.nz/

License: GNU General Public License v3.0

Java 81.84% TeX 18.05% Shell 0.09% Batchfile 0.01% Makefile 0.01% Dockerfile 0.02%

moa's Introduction

MOA (Massive Online Analysis)

Build Status Maven Central DockerHub License: GPL v3

MOA

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.

http://moa.cms.waikato.ac.nz/

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

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

moa's People

Contributors

abifet avatar fracpete avatar joaomaiaduarte avatar csterling avatar tsabsch avatar corneliusboehm avatar jmread avatar 08volt avatar richard-moulton avatar pmgj avatar hmgomes avatar rjtsousa avatar henrygouk avatar jpbarddal avatar aosojnik avatar garawalid avatar isvani avatar silasgtcs avatar dakot avatar janvanrijn avatar hfichtenberger avatar dabrze avatar tpham93 avatar celikmustafa89 avatar vlosing avatar sh2ezo avatar sybernix avatar jagub2 avatar dinaldoap avatar bundschuh avatar

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