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Massive Online Analysis MOA

Massive Online Analysis is a framework for data stream mining including machine learning algorithms such as classification, regression, clustering, outlier detection, concept drift detection and recommender systems and tools for evaluation

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Overview Platform Social System Requirements Ratings Developer Written in Initial Release Repository License Categories Alternatives

Overview

MOA (Massive Online Analysis) is an open source framework for data stream mining including machine learning algorithms such as classification, regression, clustering, outlier detection, concept drift detection and recommender systems and tools for evaluation. MOA is written in Java and relates to WEKA project.

MOA allows to build and run experiments of machine learning or data mining on evolving data streams. It is also possible to use WEKA classifiers from MOA, and MOA classifiers and streams from WEKA.

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Platform

Social

System Requirements

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Ratings

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Developer

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, University of Waikato, Contributors

Written in

Java

Initial Release

28 June 2009

Repository

https://github.com/waikato/moa

License

GPL v3

Alternatives

Machine Learning
Apache Mahout Apache SystemML Eclipse Deeplearning4j MALLET OpenCV scikit-learn TensorFlow The Microsoft Cognitive Toolkit Torch Yooreeka Orange Apache MXNet (Incubating) Apache Spark mlpack Weka PyTorch
Data Mining
scikit-learn Yooreeka Orange ELKI OpenNN Weka

Notes

Release after thesis-release is counted as initial release.

This page was last updated with commit: Now custom 'Remarks' are allowed instead of just 'Portable' in 'Get it'; all software's source files have been updated accordingly (0cb3959)