Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support

Yan, X; Zhang, C; Zhang, S

Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support

Yan, X Zhang, C

Zhang, S

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Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2009, 36 (2 PART 2), pp. 3066 - 3076
Issue Date:: 2009-01-01

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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	2009-01-01	en_US
dc.identifier.citation	Expert Systems with Applications, 2009, 36 (2 PART 2), pp. 3066 - 3076	en_US
dc.identifier.issn	0957-4174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8957
dc.description.abstract	We design a genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. In this approach, an elaborate encoding method is developed, and the relative confidence is used as the fitness function. With genetic algorithm, a global search can be performed and system automation is implemented, because our model does not require the user-specified threshold of minimum support. Furthermore, we expand this strategy to cover quantitative association rule discovery. For efficiency, we design a generalized FP-tree to implement this algorithm. We experimentally evaluate our approach, and demonstrate that our algorithms significantly reduce the computation costs and generate interesting association rules only. © 2008 Elsevier Ltd. All rights reserved.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0667060
dc.relation.ispartof	Expert Systems with Applications	en_US
dc.relation.isbasedon	10.1016/j.eswa.2008.01.028	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support	en_US
dc.type	Journal Article
utslib.citation.volume	2 PART 2	en_US
utslib.citation.volume	36	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	080604 Database Management	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
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	2 PART 2	en_US
pubs.publication-status	Published	en_US
pubs.volume	36	en_US

Abstract:

We design a genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. In this approach, an elaborate encoding method is developed, and the relative confidence is used as the fitness function. With genetic algorithm, a global search can be performed and system automation is implemented, because our model does not require the user-specified threshold of minimum support. Furthermore, we expand this strategy to cover quantitative association rule discovery. For efficiency, we design a generalized FP-tree to implement this algorithm. We experimentally evaluate our approach, and demonstrate that our algorithms significantly reduce the computation costs and generate interesting association rules only. © 2008 Elsevier Ltd. All rights reserved.

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http://hdl.handle.net/10453/8957