On data structures for association rule discovery

Yan, X; Zhang, S; Zhang, C

On data structures for association rule discovery

Yan, X Zhang, S Zhang, C

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Publication Type:: Journal Article
Citation:: Applied Artificial Intelligence, 2007, 21 (2), pp. 57 - 79
Issue Date:: 2007-02-01

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Field	Value	Language
dc.contributor.author	Yan, X	en_US
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2007-02-01	en_US
dc.identifier.citation	Applied Artificial Intelligence, 2007, 21 (2), pp. 57 - 79	en_US
dc.identifier.issn	0883-9514	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4128
dc.description.abstract	Systematically we study data structures used to implement the algorithms of association rule mining, including hash tree, itemset tree, and FP-tree (frequent pattern tree). Further, we present a generalized FP-tree in an applied context. This assists in better understanding existing association-rule-mining strategies. In addition, we discuss and analyze experimentally the generalized k-FP-tree, and demonstrate that the generalized FP-tree reduces the computation costs significantly. This study will be useful to many association analysis tasks where one must provide really interesting rules and develop efficient algorithms for identifying association rules.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0449535
dc.relation	http://purl.org/au-research/grants/arc/DP0559536
dc.relation	http://purl.org/au-research/grants/arc/DP0667060
dc.relation.ispartof	Applied Artificial Intelligence	en_US
dc.relation.isbasedon	10.1080/08839510601115544	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	On data structures for association rule discovery	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	21	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
dc.location.activity	RMIT University, Melbourne
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	en_US
pubs.publication-status	Published	en_US
pubs.volume	21	en_US

Abstract:

Systematically we study data structures used to implement the algorithms of association rule mining, including hash tree, itemset tree, and FP-tree (frequent pattern tree). Further, we present a generalized FP-tree in an applied context. This assists in better understanding existing association-rule-mining strategies. In addition, we discuss and analyze experimentally the generalized k-FP-tree, and demonstrate that the generalized FP-tree reduces the computation costs significantly. This study will be useful to many association analysis tasks where one must provide really interesting rules and develop efficient algorithms for identifying association rules.

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http://hdl.handle.net/10453/4128