Confidence metrics for association rule mining

Yan, X; Zhang, C; Zhang, S

Confidence metrics for association rule mining

Yan, X Zhang, C

Zhang, S

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Publication Type:: Journal Article
Citation:: Applied Artificial Intelligence, 2009, 23 (8), pp. 713 - 737
Issue Date:: 2009-11-06

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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	2009-11-06	en_US
dc.identifier.citation	Applied Artificial Intelligence, 2009, 23 (8), pp. 713 - 737	en_US
dc.identifier.issn	0883-9514	en_US
dc.identifier.uri	http://hdl.handle.net/10453/9056
dc.description.abstract	We propose a simple, novel, and yet effective confidence metric for measuring the interestingness of association rules. Distinguishing from existing confidence measures, our metrics really indicate the positively companionate correlations between frequent itemsets. Furthermore, some desired properties are derived for examining the goodness of confidence measures in terms of probabilistic significance. We systematically analyze our metrics and traditional ones, and demonstrate that our new algorithm significantly captures the mainstream properties. Our approach will be useful to many association analysis tasks where one must provide actionable association rules and assist users to make quality decisions.	en_US
dc.relation.ispartof	Applied Artificial Intelligence	en_US
dc.relation.isbasedon	10.1080/08839510903208062	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Confidence metrics for association rule mining	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	23	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
dc.location.activity	ISI:000270544800001	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	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	23	en_US

Abstract:

We propose a simple, novel, and yet effective confidence metric for measuring the interestingness of association rules. Distinguishing from existing confidence measures, our metrics really indicate the positively companionate correlations between frequent itemsets. Furthermore, some desired properties are derived for examining the goodness of confidence measures in terms of probabilistic significance. We systematically analyze our metrics and traditional ones, and demonstrate that our new algorithm significantly captures the mainstream properties. Our approach will be useful to many association analysis tasks where one must provide actionable association rules and assist users to make quality decisions.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/9056