Discovering interesting association rules by clustering

Publisher:
Springer
Publication Type:
Conference Proceeding
Citation:
17th Australian Joint Conference on Artificial Intelligence Proceedings, 2004, pp. 1055 - 1061
Issue Date:
2004-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2004001587.pdf112.19 kB
Adobe PDF
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.
Please use this identifier to cite or link to this item: