| 词条 | Massive Online Analysis |
| 释义 |
| name = MOA | developer = University of Waikato | latest release version = 2014.11 | latest release date = 2014/11/30 | operating system = Cross-platform | genre = Machine Learning | license = GNU General Public License | website = {{url|http://moa.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.[1] DescriptionMOA 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:
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{{Portal|Free and open-source software}}
References1. ^{{cite journal |last1=Bifet |first1=Albert |last2=Holmes |first2=Geoff |last3=Kirkby |first3=Richard |last4=Pfahringer |first4=Bernhard |title=MOA: Massive online analysis |journal=The Journal of Machine Learning Research |volume=99 |pages=1601–1604|year=2010}} 2. ^{{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}} 3. ^{{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}} 4. ^{{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}} 5. ^{{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}} 6. ^{{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}} 7. ^{{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}} 8. ^{{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}} 9. ^{{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}} 10. ^{{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}} 11. ^{{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}} 12. ^{{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}} External links
4 : Data mining and machine learning software|Free science software|Software programmed in Java (programming language)|Free data analysis software |
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