Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms

被引：109

作者：

Alcala-Fdez, Jesus ^{[1
]}

Alcala, Rafael ^{[1
]}

Jose Gacto, Maria ^{[1
]}

Herrera, Francisco ^{[1
]}

机构：

[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain

来源：

FUZZY SETS AND SYSTEMS | 2009年 / 160卷 / 07期

关键词：

Data mining; Fuzzy association rules; Genetic algorithms; Genetic fuzzy systems; 2-Tuples linguistic representation; TRADE-OFF; SYSTEMS; REPRESENTATION; FRAMEWORK; NUMBER; MODEL;

D O I：

10.1016/j.fss.2008.05.012

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Different studies have proposed methods for mining fuzzy association rules from quantitative data, where the membership functions were assumed to be known in advance. However, it is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for mining fuzzy association rules. This paper thus presents a new fuzzy data-mining algorithm for extracting both fuzzy association rules and membership functions by means of a genetic learning of the membership functions and a basic method for mining fuzzy association rules. It is based on the 2-tuples linguistic representation model allowing us to adjust the context associated to the linguistic term membership functions. Experimental results show the effectiveness of the framework. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：905 / 921

页数：17

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