Moa
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Moa | 542 | 78 | 10 | 10 days ago | 17 | July 18, 2021 | 35 | gpl-3.0 | Java | |
| 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. | ||||||||||
| Streamdm | 456 | 2 years ago | 4 | apache-2.0 | Scala | |||||
| Stream Data Mining Library for Spark Streaming | ||||||||||
| Twitch Channel Points Miner | 137 | 2 months ago | 7 | gpl-3.0 | Python | |||||
| A simple script that will watch a stream for you and get the channel points | ||||||||||
| Brewery | 136 | 10 years ago | 18 | other | Python | |||||
| IMPORTANT: Data Brewery is now Bubbles: https://github.com/stiivi/bubbles This brewery repository is NOT MAINTAINED any more. | ||||||||||
| Tornado | 104 | 10 months ago | mit | Python | ||||||
| The Tornado :tornado: framework, designed and implemented for adaptive online learning and data stream mining in Python. | ||||||||||
| Stream | 36 | 3 | 3 | a month ago | 22 | December 02, 2020 | 1 | R | ||
| A framework for data stream modeling and associated data mining tasks such as clustering and classification. - R Package | ||||||||||
| Rmoa | 34 | a year ago | 6 | R | ||||||
| Connect R to MOA for massive online data stream mining | ||||||||||
| Sentinel | 15 | 8 years ago | Java | |||||||
| Scalable real-time stream mining on Twitter Public Stream using SAMOA | ||||||||||
| Sketches | 13 | 9 years ago | Python | |||||||
| HyperLogLog and other probabilistic data structures for mining in data streams | ||||||||||
| Graphzip | 9 | 6 years ago | mit | Python | ||||||
| Mining graph streams using dictionary-based compression | ||||||||||
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.
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
