Stream data mining using the MOA framework

Philipp Kranen; Hardy Kremer; Timm Jansen; Thomas Seidl; Albert Bifet; Geoff Holmes; Bernhard Pfahringer; Jesse Read

doi:10.1007/978-3-642-29035-0_27

Stream data mining using the MOA framework

Philipp Kranen
, Hardy Kremer
, Timm Jansen
, Thomas Seidl
, Albert Bifet
, Geoff Holmes
, Bernhard Pfahringer
, Jesse Read

RWTH Aachen University
University of Waikato

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

Massive Online Analysis (MOA) is a software framework that provides algorithms and evaluation methods for mining tasks on evolving data streams. In addition to supervised and unsupervised learning, MOA has recently been extended to support multi-label classification and graph mining. In this demonstrator we describe the main features of MOA and present the newly added methods for outlier detection on streaming data. Algorithms can be compared to established baseline methods such as LOF and ABOD using standard ranking measures including Spearman rank coefficient and the AUC measure. MOA is an open source project and videos as well as tutorials are publicly available on the MOA homepage.

langue originale	Anglais
titre	Database Systems for Advanced Applications - 17th International Conference, DASFAA 2012, Proceedings
Pages	309-313
Nombre de pages	5
Edition	PART 2
Les DOIs	https://doi.org/10.1007/978-3-642-29035-0_27
état	Publié - 11 mai 2012
Modification externe	Oui
Evénement	17th International Conference on Database Systems for Advanced Applications, DASFAA 2012 - Busan, Corée du Sud Durée: 15 avr. 2012 → 18 avr. 2012

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
nombre	PART 2
Volume	7239 LNCS
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	17th International Conference on Database Systems for Advanced Applications, DASFAA 2012
Pays/Territoire	Corée du Sud
La ville	Busan
période	15/04/12 → 18/04/12

Accès au document

10.1007/978-3-642-29035-0_27

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Kranen, P., Kremer, H., Jansen, T., Seidl, T., Bifet, A., Holmes, G., Pfahringer, B., & Read, J. (2012). Stream data mining using the MOA framework. Dans Database Systems for Advanced Applications - 17th International Conference, DASFAA 2012, Proceedings (PART 2 Ed., p. 309-313). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 7239 LNCS, Numéro PART 2). https://doi.org/10.1007/978-3-642-29035-0_27

Kranen, Philipp ; Kremer, Hardy ; Jansen, Timm et al. / Stream data mining using the MOA framework. Database Systems for Advanced Applications - 17th International Conference, DASFAA 2012, Proceedings. PART 2. Ed. 2012. p. 309-313 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{38ff076957214556813a77ca49d8761a,

title = "Stream data mining using the MOA framework",

abstract = "Massive Online Analysis (MOA) is a software framework that provides algorithms and evaluation methods for mining tasks on evolving data streams. In addition to supervised and unsupervised learning, MOA has recently been extended to support multi-label classification and graph mining. In this demonstrator we describe the main features of MOA and present the newly added methods for outlier detection on streaming data. Algorithms can be compared to established baseline methods such as LOF and ABOD using standard ranking measures including Spearman rank coefficient and the AUC measure. MOA is an open source project and videos as well as tutorials are publicly available on the MOA homepage.",

author = "Philipp Kranen and Hardy Kremer and Timm Jansen and Thomas Seidl and Albert Bifet and Geoff Holmes and Bernhard Pfahringer and Jesse Read",

year = "2012",

month = may,

day = "11",

doi = "10.1007/978-3-642-29035-0\_27",

language = "English",

isbn = "9783642290343",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

pages = "309--313",

booktitle = "Database Systems for Advanced Applications - 17th International Conference, DASFAA 2012, Proceedings",

edition = "PART 2",

note = "17th International Conference on Database Systems for Advanced Applications, DASFAA 2012 ; Conference date: 15-04-2012 Through 18-04-2012",

}

Kranen, P, Kremer, H, Jansen, T, Seidl, T, Bifet, A, Holmes, G, Pfahringer, B & Read, J 2012, Stream data mining using the MOA framework. Dans Database Systems for Advanced Applications - 17th International Conference, DASFAA 2012, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Numéro PART 2, VOL. 7239 LNCS, p. 309-313, 17th International Conference on Database Systems for Advanced Applications, DASFAA 2012, Busan, Corée du Sud, 15/04/12. https://doi.org/10.1007/978-3-642-29035-0_27

Stream data mining using the MOA framework. / Kranen, Philipp; Kremer, Hardy; Jansen, Timm et al.
Database Systems for Advanced Applications - 17th International Conference, DASFAA 2012, Proceedings. PART 2. Ed. 2012. p. 309-313 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 7239 LNCS, Numéro PART 2).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Stream data mining using the MOA framework

AU - Kranen, Philipp

AU - Kremer, Hardy

AU - Jansen, Timm

AU - Seidl, Thomas

AU - Bifet, Albert

AU - Holmes, Geoff

AU - Pfahringer, Bernhard

AU - Read, Jesse

PY - 2012/5/11

Y1 - 2012/5/11

N2 - Massive Online Analysis (MOA) is a software framework that provides algorithms and evaluation methods for mining tasks on evolving data streams. In addition to supervised and unsupervised learning, MOA has recently been extended to support multi-label classification and graph mining. In this demonstrator we describe the main features of MOA and present the newly added methods for outlier detection on streaming data. Algorithms can be compared to established baseline methods such as LOF and ABOD using standard ranking measures including Spearman rank coefficient and the AUC measure. MOA is an open source project and videos as well as tutorials are publicly available on the MOA homepage.

AB - Massive Online Analysis (MOA) is a software framework that provides algorithms and evaluation methods for mining tasks on evolving data streams. In addition to supervised and unsupervised learning, MOA has recently been extended to support multi-label classification and graph mining. In this demonstrator we describe the main features of MOA and present the newly added methods for outlier detection on streaming data. Algorithms can be compared to established baseline methods such as LOF and ABOD using standard ranking measures including Spearman rank coefficient and the AUC measure. MOA is an open source project and videos as well as tutorials are publicly available on the MOA homepage.

UR - https://www.scopus.com/pages/publications/84860668502

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DO - 10.1007/978-3-642-29035-0_27

M3 - Conference contribution

AN - SCOPUS:84860668502

SN - 9783642290343

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Database Systems for Advanced Applications - 17th International Conference, DASFAA 2012, Proceedings

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Kranen P, Kremer H, Jansen T, Seidl T, Bifet A, Holmes G et al. Stream data mining using the MOA framework. Dans Database Systems for Advanced Applications - 17th International Conference, DASFAA 2012, Proceedings. PART 2 Ed. 2012. p. 309-313. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-29035-0_27

Stream data mining using the MOA framework

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Une conférence

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