Discovering interesting association rules by clustering

Zhao, Y; Zhang, C; Zhang, S

Discovering interesting association rules by clustering

Zhao, Y

Zhang, C

Zhang, S

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2004, 3339 pp. 1055 - 1061
Issue Date:: 2004-12-01

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Field	Value	Language
dc.contributor.author	Zhao, Y https://orcid.org/0000-0002-0209-3971	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 Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2004, 3339 pp. 1055 - 1061	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1967
dc.description.abstract	There are a great many metrics available for measuring the interestingness of rules. In this paper, we design a distinct approach for identifying association rules that maximizes the interestingness in an applied context. More specifically, the interestingness of association rules is defined as the dissimilarity between corresponding clusters. In addition, the interestingness assists in filtering out those rules that may be uninteresting in applications. Experiments show the effectiveness of our algorithm. © Springer-Verlag Berlin Heidelberg 2004.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0449535
dc.relation.ispartof	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Discovering interesting association rules by clustering	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3339	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Cairns,Australia	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	3339	en_US

Abstract:

There are a great many metrics available for measuring the interestingness of rules. In this paper, we design a distinct approach for identifying association rules that maximizes the interestingness in an applied context. More specifically, the interestingness of association rules is defined as the dissimilarity between corresponding clusters. In addition, the interestingness assists in filtering out those rules that may be uninteresting in applications. Experiments show the effectiveness of our algorithm. © Springer-Verlag Berlin Heidelberg 2004.

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

http://hdl.handle.net/10453/1967