Mining direct and indirect weighted fuzzy association rules in large transaction databases

Authors:
Weimin Ouyang;Qinhua Huang
Affiliations:
Modern Education Technology Center, Shanghai University of Political Science and Law, Shanghai, China;Modern Education Technology Center, Shanghai University of Political Science and Law, Shanghai, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Year:
2009

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Abstract

Association rule is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining association rules are built on the binary attributes databases, which has three limitations. Firstly, it can not concern quantitative attributes; secondly, it treats each item with the same significance although different item may have different significance; thirdly, only the direct association rules are discovered. Mining fuzzy association rules has been proposed to address the first limitation. In this paper, we put forward an idea for mining indirect weighted association rules to resolve the other two limitations, and a discovery algorithm for mining both direct and indirect weighted fuzzy association rules by integrating these three extensions.