Massive Online Analysis

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Developer(s)University of Waikato
Stable release
20.12.0[1] / 16 December 2020; 18 months ago (16 December 2020)
Operating systemCross-platform
TypeMachine Learning
LicenseGNU General Public License

Massive Online Analysis (MOA) is a free open-source software project specific for data stream mining with concept drift. It is written in Java and developed at the University of Waikato, New Zealand.[2]


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:

  • Classification
    • Bayesian classifiers
      • Naive Bayes
      • Naive Bayes Multinomial
    • Decision trees classifiers
      • Decision Stump
      • Hoeffding Tree
      • Hoeffding Option Tree
      • Hoeffding Adaptive Tree
    • Meta classifiers
      • Bagging
      • Boosting
      • Bagging using ADWIN
      • Bagging using Adaptive-Size Hoeffding Trees.
      • Perceptron Stacking of Restricted Hoeffding Trees
      • Leveraging Bagging
      • Online Accuracy Updated Ensemble
    • Function classifiers
    • Drift classifiers
      • Self-Adjusting Memory[3]
      • Probabilistic Adaptive Windowing
    • Multi-label classifiers[4]
    • Active learning classifiers [5]
  • Regression
  • Clustering[8]
    • StreamKM++
    • CluStream
    • ClusTree
    • D-Stream
    • CobWeb.
  • Outlier detection[9]
    • STORM
    • Abstract-C
    • COD
    • MCOD
    • AnyOut[10]
  • Recommender systems
    • BRISMFPredictor
  • Frequent pattern mining
  • Change detection algorithms[13]

These algorithms are designed for large scale machine learning, dealing with concept drift, and big data streams in real time.

MOA supports bi-directional interaction with Weka (machine learning). MOA is free software released under the GNU GPL.

See also[edit]


  1. ^ "Release 20.12.0". 16 December 2020. Retrieved 13 January 2021.
  2. ^ Bifet, Albert; Holmes, Geoff; Kirkby, Richard; Pfahringer, Bernhard (2010). "MOA: Massive online analysis". The Journal of Machine Learning Research. 99: 1601–1604.
  3. ^ Losing, Viktor; Hammer, Barbara; Wersing, Heiko (2017). "Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM)". Knowledge and Information Systems. 54: 171–201. doi:10.1007/s10115-017-1137-y. ISSN 0885-6125. S2CID 29600755.
  4. ^ Read, Jesse; Bifet, Albert; Holmes, Geoff; Pfahringer, Bernhard (2012). "Scalable and efficient multi-label classification for evolving data streams". Machine Learning. 88 (1–2): 243–272. doi:10.1007/s10994-012-5279-6. ISSN 0885-6125. S2CID 14676146.
  5. ^ Zliobaite, Indre; Bifet, Albert; Pfahringer, Bernhard; Holmes, Geoffrey (2014). "Active Learning With Drifting Streaming Data". IEEE Transactions on Neural Networks and Learning Systems. 25 (1): 27–39. doi:10.1109/TNNLS.2012.2236570. ISSN 2162-237X. PMID 24806642. S2CID 14687075.
  6. ^ Ikonomovska, Elena; Gama, João; Džeroski, Sašo (2010). "Learning model trees from evolving data streams" (PDF). Data Mining and Knowledge Discovery. 23 (1): 128–168. doi:10.1007/s10618-010-0201-y. ISSN 1384-5810. S2CID 7114108.
  7. ^ Almeida, Ezilda; Ferreira, Carlos; Gama, João (2013). "Adaptive Model Rules from Data Streams". Advanced Information Systems Engineering. Lecture Notes in Computer Science. Vol. 8188. pp. 480–492. CiteSeerX doi:10.1007/978-3-642-40988-2_31. ISBN 978-3-642-38708-1. ISSN 0302-9743.
  8. ^ 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. ISBN 978-1-4244-9244-2. S2CID 2064336.
  9. ^ 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. ISBN 9781450320375. S2CID 1886134.
  10. ^ 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. ISBN 978-3-642-29037-4. ISSN 0302-9743.
  11. ^ 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.
  12. ^ 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. CiteSeerX doi:10.1145/2020408.2020501. ISBN 9781450308137. S2CID 8588858.
  13. ^ 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. ISBN 978-3-642-41397-1. ISSN 0302-9743.

External links[edit]