Identifying bridging rules between conceptual clusters

Authors:
Shichao Zhang;Feng Chen;Xindong Wu;Chengqi Zhang
Affiliations:
Beijing University of Aeronautics and Astronautics, China & University of Technology, Sydney, Australia;Guangxi Normal University, Guilin City, China;University of Vermont, Burlington, VT;University of Technology, Sydney, NSW, Australia
Venue:
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2006

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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.