[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5391/IJFIS.2002.2.3.210

Generalized Fuzzy Quantitative Association Rules Mining with Fuzzy Generalization Hierarchies

Lee, Keon-Myung (School of Electrical and Computer Engineering, Chungbuk National University)

Publication Information

International Journal of Fuzzy Logic and Intelligent Systems / v.2, no.3, 2002 , pp. 210-214 More about this Journal

Abstract

Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining fer taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies fer categorical attributes and generalization hierarchies of fuzzy linguistic terms fur quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights f3r attributes.

Keywords

association rule; fuzzy association rule; generalized association rule; quantitative association rule; importance weight;

Citations & Related Records

Reference

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