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On the discovery of association rules by means of evolutionary algorithms
Author(s) -
del Jesus María J.,
Gámez José A.,
González Pedro,
Puerta José M.
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.18
Subject(s) - association rule learning , categorical variable , computer science , association (psychology) , evolutionary algorithm , k optimal pattern discovery , task (project management) , artificial intelligence , machine learning , evolutionary computation , fuzzy logic , data mining , psychology , management , economics , psychotherapist
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. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 397–415 DOI: 10.1002/widm.18 This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules Technologies > Computational Intelligence