Discovering interesting association rules by clustering
- Publication Type:
- Conference Proceeding
- Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2004, 3339 pp. 1055 - 1061
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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. © Springer-Verlag Berlin Heidelberg 2004.
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