burnmg/moa-with-volatility-adaptive-classifier

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.

About

This moa version is integrated with hybrid volatility adaptive classification algorithms.

MOA (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.

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

Using MOA

• Getting Started
• Documentation
• About 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