A database-independent approach of mining association rules with genetic algorithm

Yan, X; Zhang, C; Zhang, S

A database-independent approach of mining association rules with genetic algorithm

Yan, X Zhang, C

Zhang, S

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004, 2690 pp. 882 - 886
Issue Date:: 2004-12-01

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Field	Value	Language
dc.contributor.author	Yan, X	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Zhang, S	en_US
dc.date.issued	2004-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004, 2690 pp. 882 - 886	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1772
dc.description.abstract	Apriori-like algorithms for association rules mining rely upon the minimum support and the minimum confidence. Users often feel hard to give these thresholds. On the other hand, genetic algorithm is effective for global searching, especially when the searching space is so large that it is hardly possible to use deterministic searching method. We try to apply genetic algorithm to the association rules mining and propose an evolutionary method. Computations are conducted, showing that our ARMGA model can be used for the automation of the association rule mining systems, and the ideas given in this paper are effective. © Springer-Verlag 2003.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0343109
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A database-independent approach of mining association rules with genetic algorithm	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2690	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Hong Kong, China	en_US
dc.location.activity	London, UK
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2690	en_US

Abstract:

Apriori-like algorithms for association rules mining rely upon the minimum support and the minimum confidence. Users often feel hard to give these thresholds. On the other hand, genetic algorithm is effective for global searching, especially when the searching space is so large that it is hardly possible to use deterministic searching method. We try to apply genetic algorithm to the association rules mining and propose an evolutionary method. Computations are conducted, showing that our ARMGA model can be used for the automation of the association rule mining systems, and the ideas given in this paper are effective. © Springer-Verlag 2003.

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http://hdl.handle.net/10453/1772