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.
Keywords for this software
References in zbMATH (referenced in 10 articles )
Showing results 1 to 10 of 10.
- Brzezinski, Dariusz; Piernik, Maciej: Structural XML classification in concept drifting data streams (2015)
- Žliobaitė, Indrė; Bifet, Albert; Read, Jesse; Pfahringer, Bernhard; Holmes, Geoff: Evaluation methods and decision theory for classification of streaming data with temporal dependence (2015)
- Amini, Amineh; Wah, Teh Ying; Saboohi, Hadi: On density-based data streams clustering algorithms: a survey (2014)
- Brzezinski, Dariusz; Stefanowski, Jerzy: Combining block-based and online methods in learning ensembles from concept drifting data streams (2014)
- Miller, Zachary; Dickinson, Brian; Deitrick, William; Hu, Wei; Wang, Alex Hai: Twitter spammer detection using data stream clustering (2014)
- Pears, Russel; Sakthithasan, Sripirakas; Koh, Yun Sing: Detecting concept change in dynamic data streams (2014)
- Albertini, Marcelo Keese; Fernandes De Mello, Rodrigo: Energy-based function to evaluate data stream clustering (2013)
- Deckert, Magdalena: Incremental rule-based learners for handling concept drift: an overview (2013)
- Gama, João; Sebastião, Raquel; Rodrigues, Pedro Pereira: On evaluating stream learning algorithms (2013)
- Read, Jesse; Bifet, Albert; Holmes, Geoff; Pfahringer, Bernhard: Scalable and efficient multi-label classification for evolving data streams (2012)
Further publications can be found at: http://moa.cms.waikato.ac.nz/publications/