Identifying bridging rules between conceptual clusters

Zhang, S; Chen, F; Wu, X; Zhang, C

Identifying bridging rules between conceptual clusters

Zhang, S Chen, F Wu, X Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006, 2006 pp. 815 - 820
Issue Date:: 2006-10-16

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Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Chen, F	en_US
dc.contributor.author	Wu, X	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2006-10-16	en_US
dc.identifier.citation	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006, 2006 pp. 815 - 820	en_US
dc.identifier.isbn	1595933395	en_US
dc.identifier.isbn	9781595933393	en_US
dc.identifier.uri	http://hdl.handle.net/10453/7075
dc.description.abstract	A bridging rule in this paper has its antecedent and action from different conceptual clusters. We first design two algorithms for mining bridging rules between clusters in a database, and then propose two non-linear metrics for measuring the interestingness of bridging rules. Bridging rules can be distinct from association rules (or frequent itemsets). This is because (1) bridging rules can be generated by infrequent itemsets that are pruned in association rule mining; and (2) bridging rules are measured by the importance that includes the distance between two conceptual clusters, whereas frequent itemsets are measured by only the support. Copyright 2006 ACM.	en_US
dc.relation.ispartof	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining	en_US
dc.title	Identifying bridging rules between conceptual clusters	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2006	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Philadelphia, USA	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.publication-status	Published	en_US
pubs.volume	2006	en_US

Abstract:

A bridging rule in this paper has its antecedent and action from different conceptual clusters. We first design two algorithms for mining bridging rules between clusters in a database, and then propose two non-linear metrics for measuring the interestingness of bridging rules. Bridging rules can be distinct from association rules (or frequent itemsets). This is because (1) bridging rules can be generated by infrequent itemsets that are pruned in association rule mining; and (2) bridging rules are measured by the importance that includes the distance between two conceptual clusters, whereas frequent itemsets are measured by only the support. Copyright 2006 ACM.

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http://hdl.handle.net/10453/7075