Mining bridging rules between conceptual clusters

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

Mining bridging rules between conceptual clusters

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

Wang, R

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Publication Type:: Journal Article
Citation:: Applied Intelligence, 2012, 36 (1), pp. 108 - 118
Issue Date:: 2012-01-01

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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.contributor.author	Wang, R	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	Applied Intelligence, 2012, 36 (1), pp. 108 - 118	en_US
dc.identifier.issn	0924-669X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22257
dc.description.abstract	Bridging rules take the antecedent and action from different conceptual clusters. They are distinguished from association rules (frequent itemsets) because (1) they can be generated by the infrequent itemsets that are pruned in association rule mining, and (2) they are measured by their importance including the distance between two conceptual clusters, whereas frequent itemsets are measured only by their support. In this paper, we first design two algorithms for mining bridging rules between clusters, and then propose two non-linear metrics to measure their interestingness. We evaluate these algorithms experimentally and demonstrate that our approach is promising. © 2010 Springer Science+Business Media, LLC.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0985456
dc.relation.ispartof	Applied Intelligence	en_US
dc.relation.isbasedon	10.1007/s10489-010-0247-y	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Mining bridging rules between conceptual clusters	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	36	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	36	en_US

Abstract:

Bridging rules take the antecedent and action from different conceptual clusters. They are distinguished from association rules (frequent itemsets) because (1) they can be generated by the infrequent itemsets that are pruned in association rule mining, and (2) they are measured by their importance including the distance between two conceptual clusters, whereas frequent itemsets are measured only by their support. In this paper, we first design two algorithms for mining bridging rules between clusters, and then propose two non-linear metrics to measure their interestingness. We evaluate these algorithms experimentally and demonstrate that our approach is promising. © 2010 Springer Science+Business Media, LLC.

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http://hdl.handle.net/10453/22257