Discovering interesting association rules by clustering

Authors:
Yanchang Zhao;Chengqi Zhang;Shichao Zhang
Affiliations:
Faculty of Information Technology, Univ of Technology, Sydney, Australia;Faculty of Information Technology, Univ of Technology, Sydney, Australia;Faculty of Information Technology, Univ of Technology, Sydney, Australia
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

Citing 8
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Abstract

There are a great many metrics available for measuring the interestingness of rules In this paper, we design a distinct approach for identifying association rules that maximizes the interestingness in an applied context More specifically, the interestingness of association rules is defined as the dissimilarity between corresponding clusters In addition, the interestingness assists in filtering out those rules that may be uninteresting in applications Experiments show the effectiveness of our algorithm.