Pruning redundant association rules using maximum entropy principle

Szymon Jaroszewicz; Dan A. Simovici

doi:10.1007/3-540-47887-6_13

Pruning redundant association rules using maximum entropy principle

Szymon Jaroszewicz
, 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	Advances in Knowledge Discovery and Data Mining - 6th Pacific-Asia Conference, PAKDD 2002, Proceedings
Editors	Ming-Syan Chen, Philip S. Yu, Bing Liu
Publisher	Springer Verlag
Pages	135-147
Number of pages	13
ISBN (Print)	9783540437048
DOIs	https://doi.org/10.1007/3-540-47887-6_13
State	Published - 2002
Event	6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2002 - Taipei, Taiwan, Province of China Duration: May 6 2002 → May 8 2002

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2336
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2002
Country/Territory	Taiwan, Province of China
City	Taipei
Period	5/6/02 → 5/8/02

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Association rule
Maximum entropy
Rule interestingness
Rule pruning

Access to Document

10.1007/3-540-47887-6_13

Cite this

Jaroszewicz, S., & Simovici, D. A. (2002). Pruning redundant association rules using maximum entropy principle. In M.-S. Chen, P. S. Yu, & B. Liu (Eds.), Advances in Knowledge Discovery and Data Mining - 6th Pacific-Asia Conference, PAKDD 2002, Proceedings (pp. 135-147). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2336). Springer Verlag. https://doi.org/10.1007/3-540-47887-6_13

Pruning redundant association rules using maximum entropy principle. / Jaroszewicz, Szymon; Simovici, Dan A.
Advances in Knowledge Discovery and Data Mining - 6th Pacific-Asia Conference, PAKDD 2002, Proceedings. ed. / Ming-Syan Chen; Philip S. Yu; Bing Liu. Springer Verlag, 2002. p. 135-147 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2336).

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

Jaroszewicz, S & Simovici, DA 2002, Pruning redundant association rules using maximum entropy principle. in M-S Chen, PS Yu & B Liu (eds), Advances in Knowledge Discovery and Data Mining - 6th Pacific-Asia Conference, PAKDD 2002, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2336, Springer Verlag, pp. 135-147, 6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2002, Taipei, Taiwan, Province of China, 5/6/02. https://doi.org/10.1007/3-540-47887-6_13

Jaroszewicz S, Simovici DA. Pruning redundant association rules using maximum entropy principle. In Chen MS, Yu PS, Liu B, editors, Advances in Knowledge Discovery and Data Mining - 6th Pacific-Asia Conference, PAKDD 2002, Proceedings. Springer Verlag. 2002. p. 135-147. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-47887-6_13

Jaroszewicz, Szymon ; Simovici, Dan A. / Pruning redundant association rules using maximum entropy principle. Advances in Knowledge Discovery and Data Mining - 6th Pacific-Asia Conference, PAKDD 2002, Proceedings. editor / Ming-Syan Chen ; Philip S. Yu ; Bing Liu. Springer Verlag, 2002. pp. 135-147 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Pruning redundant association rules using maximum entropy principle

Publication series

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ASJC Scopus Subject Areas

Keywords

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