Mining bridging rules between conceptual clusters

Publication Type:
Journal Article
Applied Intelligence, 2012, 36 (1), pp. 108 - 118
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011008136OK.pdf590.3 kB
Adobe PDF
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.
Please use this identifier to cite or link to this item: