ARMGA: Identifying interesting association rules with genetic algorithms

Yan, X; Zhang, C; Zhang, S

ARMGA: Identifying interesting association rules with genetic algorithms

Yan, X Zhang, C

Zhang, S

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Publication Type:: Journal Article
Citation:: Applied Artificial Intelligence, 2005, 19 (7), pp. 677 - 689
Issue Date:: 2005-08-01

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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	2005-08-01	en_US
dc.identifier.citation	Applied Artificial Intelligence, 2005, 19 (7), pp. 677 - 689	en_US
dc.identifier.issn	0883-9514	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3969
dc.description.abstract	Priori-like algorithms for association rules mining have relied on two user-specified thresholds: minimum support and minimum confidence. There are two significant challenges to applying these algorithms to real-world applications: database-dependent minimum-support and exponential search space. Database-dependent minimum-support means that users must specify suitable thresholds for their mining tasks though they may have no knowledge concerning their databases. To circumvent these problems, in this paper, we design an evolutionary mining strategy, namely the ARMGA model, based on a genetic algorithm. Like general genetic algorithms, our ARMGA model is effective for global searching, especially when the search space is so large that it is hardly possible to use deterministic searching method. Copyright © 2005 Taylor & Francis Inc.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0449535
dc.relation.ispartof	Applied Artificial Intelligence	en_US
dc.relation.isbasedon	10.1080/08839510590967316	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	ARMGA: Identifying interesting association rules with genetic algorithms	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	19	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
dc.location.activity	Sydney
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.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

Priori-like algorithms for association rules mining have relied on two user-specified thresholds: minimum support and minimum confidence. There are two significant challenges to applying these algorithms to real-world applications: database-dependent minimum-support and exponential search space. Database-dependent minimum-support means that users must specify suitable thresholds for their mining tasks though they may have no knowledge concerning their databases. To circumvent these problems, in this paper, we design an evolutionary mining strategy, namely the ARMGA model, based on a genetic algorithm. Like general genetic algorithms, our ARMGA model is effective for global searching, especially when the search space is so large that it is hardly possible to use deterministic searching method. Copyright © 2005 Taylor & Francis Inc.

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http://hdl.handle.net/10453/3969