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{{notability|date=May 2013}}
{{notability|Products|date=May 2013}}
{{Infobox software
{{Infobox software
| name = MOA
| name = MOA
| developer = [[University of Waikato]]
| developer = [[University of Waikato]]
| latest release version = 2013.11
| latest release version = {{wikidata|property|reference|P348}}
| latest release date = 2013/11/01
| latest release date = {{start date and age|{{wikidata|qualifier|P348|P577}}}}
| operating system = [[Cross-platform]]
| operating system = [[Cross-platform]]
| genre = [[Machine Learning]]
| genre = [[Machine Learning]]
| license = [[GNU General Public License]]
| license = [[GNU General Public License]]
| website = http://moa.cms.waikato.ac.nz/
| website = {{url|http://moa.cms.waikato.ac.nz/}}

}}
}}


'''MOA (Massive Online Analysis)'''<ref name="bifet2010moa">{{cite journal
'''Massive Online Analysis''' ('''MOA''') is a free [[open-source software]] project specific for [[data stream mining]] with [[concept drift]]. It is written in [[Java (programming language)|Java]] and developed at the [[University of Waikato]], [[New Zealand]].<ref name="bifet2010moa">{{cite journal
|last1=Bifet |first1=Albert
|last1=Bifet |first1=Albert
|last2=Holmes |first2=Geoff
|last2=Holmes |first2=Geoff
Line 21: Line 20:
|volume=99
|volume=99
|pages=1601–1604|year=2010
|pages=1601–1604|year=2010
}}</ref>
}}</ref> is a free open-source software specific for [[Data stream mining]] with [[Concept drift]]. It's written in [[Java (programming language)|Java]] and developed at the [[University of Waikato]], [[New Zealand]].


==Description==
==Description==
⚫
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 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:
MOA contains several collections of machine learning algorithms:


Line 51: Line 50:
*** Pegasos
*** Pegasos
** Drift classifiers
** Drift classifiers
** Multi-label classifiers<ref name="ReadBifet2012">{{cite journal|last1=Read|first1=Jesse|last2=Bifet|first2=Albert|last3=Holmes|first3=Geoff|last4=Pfahringer|first4=Bernhard|title=Scalable and efficient multi-label classification for evolving data streams|journal=Machine Learning|volume=88|issue=1-2|year=2012|pages=243–272|issn=0885-6125|doi=10.1007/s10994-012-5279-6}}</ref>
***Self-Adjusting Memory<ref name="LosingHammer2017">{{cite journal|last1=Losing|first1=Viktor|last2=Hammer|first2=Barbara|last3=Wersing|first3=Heiko|title=Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM)|journal=Knowledge and Information Systems|volume=54|pages=171–201|year=2017|issn=0885-6125|doi=10.1007/s10115-017-1137-y|s2cid=29600755}}</ref>
***Probabilistic Adaptive Windowing
⚫
** [[Active_learning_(machine_learning)|Active learning]] classifiers <ref name="ZliobaiteBifet2014">{{cite journal|last1=Zliobaite|first1=Indre|last2=Bifet|first2=Albert|last3=Pfahringer|first3=Bernhard|last4=Holmes|first4=Geoffrey|title=Active Learning With Drifting Streaming Data|journal=IEEE Transactions on Neural Networks and Learning Systems|volume=25|issue=1|year=2014|pages=27–39|issn=2162-237X|doi=10.1109/TNNLS.2012.2236570}}</ref>
** Multi-label classifiers<ref name="ReadBifet2012">{{cite journal|last1=Read|first1=Jesse|last2=Bifet|first2=Albert|last3=Holmes|first3=Geoff|last4=Pfahringer|first4=Bernhard|title=Scalable and efficient multi-label classification for evolving data streams|journal=Machine Learning|volume=88|issue=1–2|year=2012|pages=243–272|issn=0885-6125|doi=10.1007/s10994-012-5279-6|s2cid=14676146|doi-access=free}}</ref>
⚫
** [[Active learning (machine learning)|Active learning]] classifiers <ref name="ZliobaiteBifet2014">{{cite journal|last1=Zliobaite|first1=Indre|last2=Bifet|first2=Albert|last3=Pfahringer|first3=Bernhard|last4=Holmes|first4=Geoffrey|title=Active Learning With Drifting Streaming Data|journal=IEEE Transactions on Neural Networks and Learning Systems|volume=25|issue=1|year=2014|pages=27–39|issn=2162-237X|doi=10.1109/TNNLS.2012.2236570|pmid=24806642|s2cid=14687075}}</ref>
* [[Regression analysis|Regression]]
* [[Regression analysis|Regression]]
** FIMTDD<ref name="IkonomovskaGama2010">{{cite journal|last1=Ikonomovska|first1=Elena|last2=Gama|first2=João|last3=Džeroski|first3=Sašo|title=Learning model trees from evolving data streams|journal=Data Mining and Knowledge Discovery|volume=23|issue=1|year=2010|pages=128–168|issn=1384-5810|doi=10.1007/s10618-010-0201-y}}</ref>
** FIMTDD<ref name="IkonomovskaGama2010">{{cite journal|last1=Ikonomovska|first1=Elena|last2=Gama|first2=João|last3=Džeroski|first3=Sašo|title=Learning model trees from evolving data streams|journal=Data Mining and Knowledge Discovery|volume=23|issue=1|year=2010|pages=128–168|issn=1384-5810|doi=10.1007/s10618-010-0201-y|s2cid=7114108|url=http://repositorio.inesctec.pt/bitstream/123456789/2929/1/PS-08759.pdf}}</ref>
** AMRules<ref name="AlmeidaFerreira2013">{{cite journal|last1=Almeida|first1=Ezilda|last2=Ferreira|first2=Carlos|last3=Gama|first3=João|title=Adaptive Model Rules from Data Streams|volume=8188|year=2013|pages=480–492|issn=0302-9743|doi=10.1007/978-3-642-40988-2_31}}</ref>
** AMRules<ref name="AlmeidaFerreira2013">{{cite book|last1=Almeida|first1=Ezilda|title=Advanced Information Systems Engineering|last2=Ferreira|first2=Carlos|last3=Gama|first3=João|chapter=Adaptive Model Rules from Data Streams|volume=8188|year=2013|pages=480–492|issn=0302-9743|doi=10.1007/978-3-642-40988-2_31|series=Lecture Notes in Computer Science|isbn=978-3-642-38708-1|citeseerx=10.1.1.638.5472}}</ref>
* [[data clustering|Clustering]]<ref name="KranenKremer2010">{{cite journal|last1=Kranen|first1=Philipp|last2=Kremer|first2=Hardy|last3=Jansen|first3=Timm|last4=Seidl|first4=Thomas|last5=Bifet|first5=Albert|last6=Holmes|first6=Geoff|last7=Pfahringer|first7=Bernhard|title=Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA|year=2010|pages=1400–1403|doi=10.1109/ICDMW.2010.17}}</ref>
* [[data clustering|Clustering]]<ref name="KranenKremer2010">{{cite book|last1=Kranen|first1=Philipp|title=2010 IEEE International Conference on Data Mining Workshops|last2=Kremer|first2=Hardy|last3=Jansen|first3=Timm|last4=Seidl|first4=Thomas|last5=Bifet|first5=Albert|last6=Holmes|first6=Geoff|last7=Pfahringer|first7=Bernhard|chapter=Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA|year=2010|pages=1400–1403|doi=10.1109/ICDMW.2010.17|isbn=978-1-4244-9244-2|s2cid=2064336}}</ref>
** StreamKM++
** StreamKM++
** CluStream
** CluStream
** ClusTree
** ClusTree
** D-Stream
** D-Stream
** CobWeb.
** CobWeb.
* Outlier detection<ref name="GeorgiadisKontaki2013">{{cite journal|last1=Georgiadis|first1=Dimitrios|last2=Kontaki|first2=Maria|last3=Gounaris|first3=Anastasios|last4=Papadopoulos|first4=Apostolos N.|last5=Tsichlas|first5=Kostas|last6=Manolopoulos|first6=Yannis|title=Continuous outlier detection in data streams|year=2013|pages=1061|doi=10.1145/2463676.2463691}}</ref>
* Outlier detection<ref name="GeorgiadisKontaki2013">{{cite book|last1=Georgiadis|first1=Dimitrios|title=Proceedings of the 2013 international conference on Management of data - SIGMOD '13|last2=Kontaki|first2=Maria|last3=Gounaris|first3=Anastasios|last4=Papadopoulos|first4=Apostolos N.|last5=Tsichlas|first5=Kostas|last6=Manolopoulos|first6=Yannis|chapter=Continuous outlier detection in data streams|year=2013|pages=1061|doi=10.1145/2463676.2463691|isbn=9781450320375|s2cid=1886134}}</ref>
** STORM
** STORM
** Abstract-C
** Abstract-C
** COD
** COD
** MCOD
** MCOD
** AnyOut<ref name="AssentKranen2012">{{cite journal|last1=Assent|first1=Ira|last2=Kranen|first2=Philipp|last3=Baldauf|first3=Corinna|last4=Seidl|first4=Thomas|title=AnyOut: Anytime Outlier Detection on Streaming Data|volume=7238|year=2012|pages=228–242|issn=0302-9743|doi=10.1007/978-3-642-29038-1_18}}</ref>
** AnyOut<ref name="AssentKranen2012">{{cite book|last1=Assent|first1=Ira|title=Database Systems for Advanced Applications|last2=Kranen|first2=Philipp|last3=Baldauf|first3=Corinna|last4=Seidl|first4=Thomas|chapter=AnyOut: Anytime Outlier Detection on Streaming Data|volume=7238|year=2012|pages=228–242|issn=0302-9743|doi=10.1007/978-3-642-29038-1_18|series=Lecture Notes in Computer Science|isbn=978-3-642-29037-4}}</ref>
* [[Recommender system|Recommender systems]]
* [[Recommender system]]s
** BRISMFPredictor
** BRISMFPredictor
* Frequent pattern mining
* [[Frequent pattern mining]]
** Itemsets<ref name="Quadrana2013">{{cite journal|last1=Quadrana|first1=Massimo|last2=Bifet|first2=Albert|last3=Gavaldà|first3=Ricard|title=An Efficient Closed Frequent Itemset Miner for the MOA Stream Mining System|year=2013|pages=203|doi=10.3233/978-1-61499-320-9-203}}</ref>
** Itemsets<ref name="Quadrana2013">{{cite journal|last1=Quadrana|first1=Massimo|last2=Bifet|first2=Albert|last3=Gavaldà|first3=Ricard|title=An Efficient Closed Frequent Itemset Miner for the MOA Stream Mining System|journal=Frontiers in Artificial Intelligence and Applications|volume=256|issue=Artificial Intelligence Research and Development|year=2013|pages=203|doi=10.3233/978-1-61499-320-9-203}}</ref>
** Graphs<ref name="BifetHolmes2011">{{cite journal|last1=Bifet|first1=Albert|last2=Holmes|first2=Geoff|last3=Pfahringer|first3=Bernhard|last4=Gavaldà|first4=Ricard|title=Mining frequent closed graphs on evolving data streams|year=2011|pages=591|doi=10.1145/2020408.2020501}}</ref>
** Graphs<ref name="BifetHolmes2011">{{cite book|last1=Bifet|first1=Albert|title=Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11|last2=Holmes|first2=Geoff|last3=Pfahringer|first3=Bernhard|last4=Gavaldà|first4=Ricard|chapter=Mining frequent closed graphs on evolving data streams|year=2011|pages=591|doi=10.1145/2020408.2020501|isbn=9781450308137|citeseerx=10.1.1.297.1721|s2cid=8588858}}</ref>
* Change detection algorithms<ref name="BifetRead2013">{{cite journal|last1=Bifet|first1=Albert|last2=Read|first2=Jesse|last3=Pfahringer|first3=Bernhard|last4=Holmes|first4=Geoff|last5=Žliobaitė|first5=Indrė|title=CD-MOA: Change Detection Framework for Massive Online Analysis|volume=8207|year=2013|pages=92–103|issn=0302-9743|doi=10.1007/978-3-642-41398-8_9}}</ref>
* Change detection algorithms<ref name="BifetRead2013">{{cite book|last1=Bifet|first1=Albert|title=Advances in Intelligent Data Analysis XII|last2=Read|first2=Jesse|last3=Pfahringer|first3=Bernhard|last4=Holmes|first4=Geoff|last5=Žliobaitė|first5=Indrė|chapter=CD-MOA: Change Detection Framework for Massive Online Analysis|volume=8207|year=2013|pages=92–103|issn=0302-9743|doi=10.1007/978-3-642-41398-8_9|series=Lecture Notes in Computer Science|isbn=978-3-642-41397-1}}</ref>


These algorithms are designed for large scale machine learning, dealing with concept drift, and big data streams in real time.
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]].
MOA supports bi-directional interaction with [[Weka (machine learning)|Weka]]. MOA is [[free software]] released under the [[GNU GPL]].


==See also==
==See also==
{{Portal|Free software}}
{{Portal|Free and open-source software}}
* [https://adams.cms.waikato.ac.nz/ ADAMS Workflow]: Workflow engine for MOA and [[Weka (machine learning)]]
* [https://adams.cms.waikato.ac.nz/ ADAMS Workflow]: Workflow engine for MOA and Weka
* [http://www-ai.cs.uni-dortmund.de/SOFTWARE/streams/ Streams]: Flexible module environment for the design and execution of data stream experiments
* [http://www-ai.cs.uni-dortmund.de/SOFTWARE/streams/ Streams]: Flexible module environment for the design and execution of data stream experiments
* [[Weka (machine learning)]]
* [[Vowpal Wabbit]]
* [[Vowpal Wabbit]]
* [[List of numerical analysis software]]
* [[List of numerical analysis software]]
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== External links ==
== External links ==
* [http://moa.cs.waikato.ac.nz/ MOA Project home page at University of Waikato in New Zealand]
* [http://moa.cs.waikato.ac.nz/ MOA Project home page at University of Waikato in New Zealand]
* [http://samoa-project.net/ SAMOA Project home page at Yahoo Labs]
* [http://samoa-project.net/ SAMOA Project home page at Yahoo Labs]


[[Category:Data mining and machine learning software]]
[[Category:Data mining and machine learning software]]
[[Category:Free science software]]
[[Category:Free science software]]
[[Category:Software programmed in Java]]
[[Category:Java (programming language) software]]
[[Category:Free data analysis software]]
[[Category:Free data analysis software]]

Latest revision as of 15:23, 13 December 2024

MOA
Developer(s)University of Waikato
Stable release
24.07.0[1] / 18 July 2024; 5 months ago (18 July 2024)
Repository
Operating systemCross-platform
TypeMachine Learning
LicenseGNU General Public License
Websitemoa.cms.waikato.ac.nz

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]

Description

[edit]

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. MOA is free software released under the GNU GPL.

See also

[edit]

References

[edit]
  1. ^ "Release 24.07.0". 18 July 2024. Retrieved 23 July 2024.
  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 10.1.1.638.5472. 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 10.1.1.297.1721. 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.
[edit]
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