A Genetic Algorithm for Discovery of Association Rules

Author

Soto, Walt ; Olaya-Benavides, A.

Author_Institution

Intell. Syst. & Spatial Inf. Res. Group (SIGA), Central Univ., Bogota, Colombia

fYear

2011

fDate

9-11 Nov. 2011

Firstpage

289

Lastpage

293

Abstract

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.

Keywords

data mining; genetic algorithms; probability; SSOCF; association rule discovery; data mining; fitness function; genetic algorithm; nonsymmetric probability; selection strategy; subset size oriented common feature crossover operator; Accuracy; Association rules; Biological cells; Genetic algorithms; Sociology; Statistics; Association Rules; Data Mining; Genetic Algorithm; Knowledge Discovery;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science Society (SCCC), 2011 30th International Conference of the Chilean

Conference_Location

Curico

ISSN

1522-4902

Print_ISBN

978-1-4673-1364-3

Type

conf

DOI

10.1109/SCCC.2011.37

Filename

6363409