On the discovery of association rules by means of evolutionary algorithms

被引：35

作者：

del Jesus, Maria J. ^{[1
]}

Gamez, Jose A. ^{[2
]}

Gonzalez, Pedro ^{[1
]}

Puerta, Jose M. ^{[2
]}

机构：

[1] Univ Jaen, Dept Comp Sci, Jaen 23071, Spain

[2] Univ Castilla La Mancha, Comp Syst Dept, Albacete 02071, Spain

来源：

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY | 2011年 / 1卷 / 05期

关键词：

MULTIOBJECTIVE GENETIC ALGORITHM; PARTICLE SWARM OPTIMIZATION; MEMBERSHIP FUNCTIONS; CLASSIFIER SYSTEMS; EXPRESSION DATA; METAHEURISTICS; INDUCTION; TAXONOMY; MODELS;

D O I：

10.1002/widm.18

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Association rule learning is a data mining task that tries to discover interesting relations between variables in large databases. A review of association rule learning is presented that focuses on the use of evolutionary algorithms not only applied to Boolean variables but also to categorical and quantitative ones. The use of fuzzy rules in the evolutionary algorithms for association rule learning is also described. Finally, the main applications of association rule evolutionary learning covered by the specialized bibliography are reviewed. (C) 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 397-415 DOI:10.1002/widm.18

引用

页码：397 / 415

页数：19

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