Incremental frequent itemsets mining with MapReduce

Kirill Kandalov; Ehud Gudes

doi:10.1007/978-3-319-66917-5_17

Incremental frequent itemsets mining with MapReduce

Kirill Kandalov
, Ehud Gudes

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

2 Scopus citations

Abstract

Frequent itemsets mining is a common task in data mining. Since sizes of today’s databases go far beyond capabilities of a single machine, recent studies show how to adopt classical algorithms for frequent itemsets mining for parallel frameworks such as MapReduce. Even then, in case of a slight database update a re-run of the MapReduce mining algorithm from the beginning on the whole data set is required and could be far from optimal. Thus, a variation of these algorithms for incremental database update is desired. The current paper presents a general algorithm for incremental frequent itemsets mining and shows how to adapt it to the parallel paradigm. It also provides optimizations that are unique to a constrained model of MapReduce for an effective algorithm.

Original language	English
Title of host publication	Advances in Databases and Information Systems - 21st European Conference, ADBIS 2017, Proceedings
Editors	Marite Kirikova, Kjetil Norvag, George A. Papadopoulos
Publisher	Springer Verlag
Pages	247-261
Number of pages	15
ISBN (Print)	9783319669168
DOIs	https://doi.org/10.1007/978-3-319-66917-5_17
State	Published - 1 Jan 2017
Event	21st European Conference on Advances in Databases and Information Systems, ADBIS 2017 - Nicosia, Cyprus Duration: 24 Sep 2017 → 27 Sep 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10509 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st European Conference on Advances in Databases and Information Systems, ADBIS 2017
Country/Territory	Cyprus
City	Nicosia
Period	24/09/17 → 27/09/17

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-66917-5_17

Cite this

Kandalov, K., & Gudes, E. (2017). Incremental frequent itemsets mining with MapReduce. In M. Kirikova, K. Norvag, & G. A. Papadopoulos (Eds.), Advances in Databases and Information Systems - 21st European Conference, ADBIS 2017, Proceedings (pp. 247-261). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10509 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66917-5_17

Kandalov, Kirill ; Gudes, Ehud. / Incremental frequent itemsets mining with MapReduce. Advances in Databases and Information Systems - 21st European Conference, ADBIS 2017, Proceedings. editor / Marite Kirikova ; Kjetil Norvag ; George A. Papadopoulos. Springer Verlag, 2017. pp. 247-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Kandalov, K & Gudes, E 2017, Incremental frequent itemsets mining with MapReduce. in M Kirikova, K Norvag & GA Papadopoulos (eds), Advances in Databases and Information Systems - 21st European Conference, ADBIS 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10509 LNCS, Springer Verlag, pp. 247-261, 21st European Conference on Advances in Databases and Information Systems, ADBIS 2017, Nicosia, Cyprus, 24/09/17. https://doi.org/10.1007/978-3-319-66917-5_17

Incremental frequent itemsets mining with MapReduce. / Kandalov, Kirill; Gudes, Ehud.
Advances in Databases and Information Systems - 21st European Conference, ADBIS 2017, Proceedings. ed. / Marite Kirikova; Kjetil Norvag; George A. Papadopoulos. Springer Verlag, 2017. p. 247-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10509 LNCS).

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

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AB - Frequent itemsets mining is a common task in data mining. Since sizes of today’s databases go far beyond capabilities of a single machine, recent studies show how to adopt classical algorithms for frequent itemsets mining for parallel frameworks such as MapReduce. Even then, in case of a slight database update a re-run of the MapReduce mining algorithm from the beginning on the whole data set is required and could be far from optimal. Thus, a variation of these algorithms for incremental database update is desired. The current paper presents a general algorithm for incremental frequent itemsets mining and shows how to adapt it to the parallel paradigm. It also provides optimizations that are unique to a constrained model of MapReduce for an effective algorithm.

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Kandalov K, Gudes E. Incremental frequent itemsets mining with MapReduce. In Kirikova M, Norvag K, Papadopoulos GA, editors, Advances in Databases and Information Systems - 21st European Conference, ADBIS 2017, Proceedings. Springer Verlag. 2017. p. 247-261. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-66917-5_17

Incremental frequent itemsets mining with MapReduce

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