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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Database Systems for Advanced Applications - 17th International Conference, DASFAA 2012, Proceedings
Pages	309-313
Number of pages	5
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-29035-0_27
Publication status	Published - 11 May 2012
Externally published	Yes
Event	17th International Conference on Database Systems for Advanced Applications, DASFAA 2012 - Busan, Korea, Republic of Duration: 15 Apr 2012 → 18 Apr 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7239 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Database Systems for Advanced Applications, DASFAA 2012
Country/Territory	Korea, Republic of
City	Busan
Period	15/04/12 → 18/04/12

Access to Document

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

Cite this

Kranen, P., Kremer, H., Jansen, T., Seidl, T., Bifet, A., Holmes, G., Pfahringer, B., & Read, J. (2012). Stream data mining using the MOA framework. In Database Systems for Advanced Applications - 17th International Conference, DASFAA 2012, Proceedings (PART 2 ed., pp. 309-313). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7239 LNCS, No. 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. pp. 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. in 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), no. PART 2, vol. 7239 LNCS, pp. 309-313, 17th International Conference on Database Systems for Advanced Applications, DASFAA 2012, Busan, Korea, Republic of, 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, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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)

SP - 309

EP - 313

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

T2 - 17th International Conference on Database Systems for Advanced Applications, DASFAA 2012

Y2 - 15 April 2012 through 18 April 2012

ER -

Kranen P, Kremer H, Jansen T, Seidl T, Bifet A, Holmes G et al. Stream data mining using the MOA framework. In 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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