Mining weighted generalized fuzzy association rules with fuzzy taxonomies

Authors:
Shen Bin;Yao Min;Yuan Bo
Affiliations:
College of Computer, Zhejiang University, Hangzhou, China;College of Computer, Zhejiang University, Hangzhou, China;College of Computer, Zhejiang University, Hangzhou, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

This paper proposes the problem of mining weighted generalized fuzzy association rules with fuzzy taxonomies (WGF-ARs). It is an extension of the generalized fuzzy association rules with fuzzy taxonomies problem. In order to reflect the importance of different items, the notion of generalized weights is introduced, and leaf-node items and ancestor items are assigned generalized weights in our WGF-ARs. The definitions of weighted support and weighted confidence of WGF-ARs is also proposed. Then a new mining algorithm for WGF-ARs is also proposed, and several optimizations have been applied to reduce the computational complexity of the algorithm.