Efficient mining of both positive and negative association rules

Wu, X; Zhang, C; Zhang, S

Efficient mining of both positive and negative association rules

Wu, X Zhang, C

Zhang, S

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Publication Type:: Journal Article
Citation:: ACM Transactions on Information Systems, 2004, 22 (3), pp. 381 - 405
Issue Date:: 2004-07-01

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Field	Value	Language
dc.contributor.author	Wu, 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	2004-07-01	en_US
dc.identifier.citation	ACM Transactions on Information Systems, 2004, 22 (3), pp. 381 - 405	en_US
dc.identifier.issn	1046-8188	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4136
dc.description.abstract	This paper presents an efficient method for mining both positive and negative association rules in databases. The method extends traditional associations to include association rules of forms A ⇒ ¬B, ¬A ⇒ B, and ¬A ⇒ ¬B, which indicate negative associations between itemsets. With a pruning strategy and an interestingness measure, our method scales to large databases. The method has been evaluated using both synthetic and real-world databases, and our experimental results demonstrate its effectiveness and efficiency.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0343109
dc.relation.ispartof	ACM Transactions on Information Systems	en_US
dc.relation.isbasedon	10.1145/1010614.1010616	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Efficient mining of both positive and negative association rules	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	22	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0807 Library and Information Studies	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	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	22	en_US

Abstract:

This paper presents an efficient method for mining both positive and negative association rules in databases. The method extends traditional associations to include association rules of forms A ⇒ ¬B, ¬A ⇒ B, and ¬A ⇒ ¬B, which indicate negative associations between itemsets. With a pruning strategy and an interestingness measure, our method scales to large databases. The method has been evaluated using both synthetic and real-world databases, and our experimental results demonstrate its effectiveness and efficiency.

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

http://hdl.handle.net/10453/4136