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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
871:
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".
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Georgiadis, Dimitrios; Kontaki, Maria; Gounaris, Anastasios; Papadopoulos, Apostolos N.; Tsichlas, Kostas; Manolopoulos, Yannis (2013). "Continuous outlier detection in data streams".
43:
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Bifet, Albert; Read, Jesse; Pfahringer, Bernhard; Holmes, Geoff; Žliobaitė, Indrė (2013). "CD-MOA: Change
Detection Framework for Massive Online Analysis".
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of the topic and provide significant coverage of it beyond a mere trivial mention. If notability cannot be shown, the article is likely to be
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Quadrana, Massimo; Bifet, Albert; Gavaldà, Ricard (2013). "An
Efficient Closed Frequent Itemset Miner for the MOA Stream Mining System".
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Bifet, Albert; Holmes, Geoff; Pfahringer, Bernhard; Gavaldà, Ricard (2011). "Mining frequent closed graphs on evolving data streams".
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Losing, Viktor; Hammer, Barbara; Wersing, Heiko (2017). "Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM)".
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Zliobaite, Indre; Bifet, Albert; Pfahringer, Bernhard; Holmes, Geoffrey (2014). "Active
Learning With Drifting Streaming Data".
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These algorithms are designed for large scale machine learning, dealing with concept drift, and big data streams in real time.
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Assent, Ira; Kranen, Philipp; Baldauf, Corinna; Seidl, Thomas (2012). "AnyOut: Anytime
Outlier Detection on Streaming Data".
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Proceedings of the 17th ACM SIGKDD international conference on
Knowledge discovery and data mining - KDD '11
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Bifet, Albert; Holmes, Geoff; Kirkby, Richard; Pfahringer, Bernhard (2010). "MOA: Massive online analysis".
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Almeida, Ezilda; Ferreira, Carlos; Gama, João (2013). "Adaptive Model Rules from Data
Streams".
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553:: Flexible module environment for the design and execution of data stream experiments
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Proceedings of the 2013 international conference on
Management of data - SIGMOD '13
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Read, Jesse; Bifet, Albert; Holmes, Geoff; Pfahringer, Bernhard (2012).
958:. Lecture Notes in Computer Science. Vol. 7238. pp. 228–242.
824:. Lecture Notes in Computer Science. Vol. 8188. pp. 480–492.
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1077:. Lecture Notes in Computer Science. Vol. 8207. pp. 92–103.
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Please help to demonstrate the notability of the topic by citing
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MOA Project home page at
University of Waikato in New Zealand
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2010 IEEE International Conference on Data Mining Workshops
1003:(Artificial Intelligence Research and Development): 203.
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IEEE Transactions on Neural Networks and Learning Systems
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Ikonomovska, Elena; Gama, João; Džeroski, Sašo (2010).
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Frontiers in Artificial Intelligence and Applications
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several collections of machine learning algorithms:
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772:"Learning model trees from evolving data streams"
369:Perceptron Stacking of Restricted Hoeffding Trees
44:notability guidelines for products and services
504:MOA supports bi-directional interaction with
8:
366:Bagging using Adaptive-Size Hoeffding Trees.
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956:Database Systems for Advanced Applications
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1138:Data mining and machine learning software
1075:Advances in Intelligent Data Analysis XII
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129:Learn how and when to remove this message
822:Advanced Information Systems Engineering
610:The Journal of Machine Learning Research
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1123:SAMOA Project home page at Yahoo Labs
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1148:Java (programming language) software
534:Free and open-source software portal
779:Data Mining and Knowledge Discovery
567:List of numerical analysis software
25:
629:Knowledge and Information Systems
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403:Probabilistic Adaptive Windowing
375:Online Accuracy Updated Ensemble
31:
42:may not meet Knowledge (XXG)'s
544:: Workflow engine for MOA and
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1009:10.3233/978-1-61499-320-9-203
18:MOA (Massive Online Analysis)
964:10.1007/978-3-642-29038-1_18
840:10.1007/978-3-642-40988-2_31
1153:Free data analysis software
1083:10.1007/978-3-642-41398-8_9
497:Change detection algorithms
387:Stochastic gradient descent
337:Decision trees classifiers
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733:10.1109/TNNLS.2012.2236570
250:GNU General Public License
51:reliable secondary sources
40:The topic of this article
791:10.1007/s10618-010-0201-y
689:10.1007/s10994-012-5279-6
641:10.1007/s10115-017-1137-y
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169:24.07.0 / 18 July 2024
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78:"Massive Online Analysis"
1042:10.1145/2020408.2020501
923:10.1145/2463676.2463691
557:Weka (machine learning)
546:Weka (machine learning)
506:Weka (machine learning)
485:Frequent pattern mining
408:Multi-label classifiers
349:Hoeffding Adaptive Tree
332:Naive Bayes Multinomial
278:Massive Online Analysis
876:. pp. 1400–1403.
1143:Free science software
882:10.1109/ICDMW.2010.17
400:Self-Adjusting Memory
380:Function classifiers
346:Hoeffding Option Tree
326:Bayesian classifiers
302:University of Waikato
300:and developed at the
288:project specific for
171:; 59 days ago
153:University of Waikato
286:open-source software
512:released under the
475:Recommender systems
363:Bagging using ADWIN
296:. It is written in
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454:Outlier detection
397:Drift classifiers
372:Leveraging Bagging
290:data stream mining
46:
1092:978-3-642-41397-1
973:978-3-642-29037-4
891:978-1-4244-9244-2
849:978-3-642-38708-1
587:"Release 24.07.0"
354:Meta classifiers
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616:: 1601–1604.
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591:. Retrieved
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414:classifiers
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284:) is a free
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176:18 July 2024
148:Developer(s)
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635:: 171–201.
329:Naive Bayes
312:Description
306:New Zealand
55:independent
1132:Categories
573:References
460:Abstract-C
437:StreamKM++
433:Clustering
420:Regression
383:Perceptron
190:Repository
89:newspapers
63:redirected
1101:0302-9743
1028:CiteSeerX
982:0302-9743
858:0302-9743
826:CiteSeerX
799:1384-5810
741:2162-237X
698:0885-6125
649:0885-6125
508:. MOA is
440:CluStream
53:that are
757:14687075
749:24806642
706:14676146
657:29600755
520:See also
489:Itemsets
446:D-Stream
443:ClusTree
360:Boosting
265:.waikato
203:/waikato
119:May 2013
1060:8588858
941:1886134
900:2064336
807:7114108
593:23 July
551:Streams
514:GNU GPL
449:CobWeb.
427:AMRules
392:Pegasos
357:Bagging
256:Website
245:License
174: (
103:scholar
67:deleted
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492:Graphs
469:AnyOut
424:FIMTDD
199:github
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59:merged
1056:S2CID
937:S2CID
896:S2CID
803:S2CID
775:(PDF)
753:S2CID
702:S2CID
653:S2CID
457:STORM
389:(SGD)
292:with
110:JSTOR
96:books
65:, or
1097:ISSN
1087:ISBN
1046:ISBN
978:ISSN
968:ISBN
927:ISBN
886:ISBN
854:ISSN
844:ISBN
795:ISSN
745:PMID
737:ISSN
694:ISSN
645:ISSN
595:2024
466:MCOD
298:Java
263:.cms
233:Type
205:/moa
201:.com
82:news
1079:doi
1038:doi
1005:doi
1001:256
960:doi
919:doi
878:doi
836:doi
787:doi
729:doi
684:doi
637:doi
463:COD
282:MOA
269:.nz
267:.ac
261:moa
142:MOA
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Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
