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.

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Shichao Zhang, Feng Chen, Xindong Wu, Chengqi Zhang
• Keywords: Association Rule Mining, Bridging Rule, Data Mining, KDD 2006, Mining Bridging Rules