A Genetic Algorithm for Discovery of Association Rules

A genetic algorithm is proposed in this article for discovery of association rules. The main characteristics of the algorithm are: (1) The individual is represented as a set of rules (2) The fitness function is a criteria combination to evaluate the rule's quality - high precision prediction, c...

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Bibliographic Details
Published in2011 30th International Conference of the Chilean Computer Science Society pp. 289 - 293
Main Authors Soto, W., Olaya-Benavides, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2011
Subjects
Accuracy
Association rules
Association Rules; Data Mining; Genetic Algorithm; Knowledge Discovery
Biological cells
Genetic algorithms
Sociology
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ISBN9781467313643
1467313645
ISSN1522-4902
DOI10.1109/SCCC.2011.37

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Summary:A genetic algorithm is proposed in this article for discovery of association rules. The main characteristics of the algorithm are: (1) The individual is represented as a set of rules (2) The fitness function is a criteria combination to evaluate the rule's quality - high precision prediction, comprehensibility and interestingness -- (3) Subset Size-Oriented Common feature Crossover Operator (SSOCF) is used in the crossover stage (4) mutation is calculated through non-symmetric probability and selection strategy through tournament and (5) the algorithm was implemented using the library lambdaj. Finally, the genetic algorithm effectiveness and the quality of the rule in the experimental results are shown.
ISBN:9781467313643
1467313645
ISSN:1522-4902
DOI:10.1109/SCCC.2011.37