Discovering fuzzy association rules using fuzzy partition methods

Fuzzy association rules described by the natural language are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. In this paper, a new algorithm named fuzzy grids based rules mining algorith...

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Published inKnowledge-based systems Vol. 16; no. 3; pp. 137 - 147
Main Authors Hu, Yi-Chung, Chen, Ruey-Shun, Tzeng, Gwo-Hshiung
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.04.2003
Subjects
Association rules
Computer applications
Data mining
Decision making
Fuzzy partition
Fuzzy set theory
Fuzzy partition
Data mining
Association rules
Decision making
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Summary:Fuzzy association rules described by the natural language are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. In this paper, a new algorithm named fuzzy grids based rules mining algorithm (FGBRMA) is proposed to generate fuzzy association rules from a relational database. The proposed algorithm consists of two phases: one to generate the large fuzzy grids, and the other to generate the fuzzy association rules. A numerical example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstrating the effectiveness of the proposed algorithm.
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ISSN:0950-7051
1872-7409
DOI:10.1016/S0950-7051(02)00079-5