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KDD
2006
ACM

Identifying bridging rules between conceptual clusters

11 years 4 months ago
Identifying bridging rules between conceptual clusters
1 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. Categories and Subject Descriptors H.2.8 [Database Applications]: Data mining; I.2.6 Learning. General Terms: Algorithms; Measurement; Theory.
Shichao Zhang, Feng Chen, Xindong Wu, Chengqi Zhan
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2006
Where KDD
Authors Shichao Zhang, Feng Chen, Xindong Wu, Chengqi Zhang
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