On genetic algorithms for detecting frequent item sets and large bite sets

Roman A. Sizov; Dan A. Simovici

doi:10.1007/978-3-319-41920-6_34

On genetic algorithms for detecting frequent item sets and large bite sets

Roman A. Sizov
, Dan A. Simovici

Computer Science

University of Massachusetts Boston

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

Original language	English
Title of host publication	Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings
Editors	Petra Perner
Publisher	Springer Verlag
Pages	435-445
Number of pages	11
ISBN (Print)	9783319419190
DOIs	https://doi.org/10.1007/978-3-319-41920-6_34
State	Published - 2016
Event	12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016 - New York, United States Duration: Jul 16 2016 → Jul 21 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9729
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016
Country/Territory	United States
City	New York
Period	7/16/16 → 7/21/16

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Bite set
Covering
Genetic algorithm
Support set

Access to Document

10.1007/978-3-319-41920-6_34

Cite this

Sizov, R. A., & Simovici, D. A. (2016). On genetic algorithms for detecting frequent item sets and large bite sets. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings (pp. 435-445). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9729). Springer Verlag. https://doi.org/10.1007/978-3-319-41920-6_34

On genetic algorithms for detecting frequent item sets and large bite sets. / Sizov, Roman A.; Simovici, Dan A.
Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings. ed. / Petra Perner. Springer Verlag, 2016. p. 435-445 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9729).

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

Sizov, RA & Simovici, DA 2016, On genetic algorithms for detecting frequent item sets and large bite sets. in P Perner (ed.), Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9729, Springer Verlag, pp. 435-445, 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016, New York, United States, 7/16/16. https://doi.org/10.1007/978-3-319-41920-6_34

Sizov RA, Simovici DA. On genetic algorithms for detecting frequent item sets and large bite sets. In Perner P, editor, Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings. Springer Verlag. 2016. p. 435-445. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-41920-6_34

Sizov, Roman A. ; Simovici, Dan A. / On genetic algorithms for detecting frequent item sets and large bite sets. Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings. editor / Petra Perner. Springer Verlag, 2016. pp. 435-445 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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On genetic algorithms for detecting frequent item sets and large bite sets

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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