MOA is an open-source framework software that allows to build and run experiments
of machine learning or data mining on evolving data streams. It includes a set of learners and stream generators that can be used from the Graphical User Interface (GUI), the command-line, and the Java API.
MOA contains several collections of machine learning algorithms:
^Kranen, Philipp; Kremer, Hardy; Jansen, Timm; Seidl, Thomas; Bifet, Albert; Holmes, Geoff; Pfahringer, Bernhard (2010). "Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA". 2010 IEEE International Conference on Data Mining Workshops. pp. 1400–1403. doi:10.1109/ICDMW.2010.17. ISBN978-1-4244-9244-2. S2CID2064336.
^Georgiadis, Dimitrios; Kontaki, Maria; Gounaris, Anastasios; Papadopoulos, Apostolos N.; Tsichlas, Kostas; Manolopoulos, Yannis (2013). "Continuous outlier detection in data streams". Proceedings of the 2013 international conference on Management of data - SIGMOD '13. p. 1061. doi:10.1145/2463676.2463691. ISBN9781450320375. S2CID1886134.
^Assent, Ira; Kranen, Philipp; Baldauf, Corinna; Seidl, Thomas (2012). "AnyOut: Anytime Outlier Detection on Streaming Data". Database Systems for Advanced Applications. Lecture Notes in Computer Science. Vol. 7238. pp. 228–242. doi:10.1007/978-3-642-29038-1_18. ISBN978-3-642-29037-4. ISSN0302-9743.
^Quadrana, Massimo; Bifet, Albert; Gavaldà, Ricard (2013). "An Efficient Closed Frequent Itemset Miner for the MOA Stream Mining System". Frontiers in Artificial Intelligence and Applications. 256 (Artificial Intelligence Research and Development): 203. doi:10.3233/978-1-61499-320-9-203.
^Bifet, Albert; Holmes, Geoff; Pfahringer, Bernhard; Gavaldà, Ricard (2011). "Mining frequent closed graphs on evolving data streams". Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11. p. 591. CiteSeerX10.1.1.297.1721. doi:10.1145/2020408.2020501. ISBN9781450308137. S2CID8588858.
^Bifet, Albert; Read, Jesse; Pfahringer, Bernhard; Holmes, Geoff; Žliobaitė, Indrė (2013). "CD-MOA: Change Detection Framework for Massive Online Analysis". Advances in Intelligent Data Analysis XII. Lecture Notes in Computer Science. Vol. 8207. pp. 92–103. doi:10.1007/978-3-642-41398-8_9. ISBN978-3-642-41397-1. ISSN0302-9743.