Title :
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
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;
Conference_Titel :
Computer Science Society (SCCC), 2011 30th International Conference of the Chilean
Conference_Location :
Curico
Print_ISBN :
978-1-4673-1364-3
DOI :
10.1109/SCCC.2011.37
