Detection of Fuzzy Association Rules by Fuzzy Transforms | Zendy

Ferdinando Di Martino | Zendy; Salvatore Sessa | Zendy

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Detection of Fuzzy Association Rules by Fuzzy Transforms

Author(s) -

Ferdinando Di Martino,

Salvatore Sessa

Publication year - 2012

Publication title -

advances in fuzzy systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.38

H-Index - 19

eISSN - 1687-711X

pISSN - 1687-7101

DOI - 10.1155/2012/258476

Subject(s) - association rule learning , fuzzy logic , data mining , computer science , partition (number theory) , fuzzy set , fuzzy classification , fuzzy set operations , artificial intelligence , neuro fuzzy , defuzzification , pattern recognition (psychology) , mathematics , fuzzy number , fuzzy control system , combinatorics

We present a new method based on the use of fuzzy transforms for detecting coarse-grained association rules in the datasets. The fuzzy association rules are represented in the form of linguistic expressions and we introduce a pre-processing phase to determine the optimal fuzzy partition of the domains of the quantitative attributes. In the extraction of the fuzzy association rules we use the AprioriGen algorithm and a confidence index calculated via the inverse fuzzy transform. Our method is applied to datasets of the 2001 census database of the district of Naples (Italy); the results show that the extracted fuzzy association rules provide a correct coarse-grained view of the data association rule set

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