An Algorithm for Mining Association Rules Based on Improved Genetic Algorithm and its Application

Authors:
Hong Guo;Ya Zhou
Affiliations:
-;-
Venue:
WGEC '09 Proceedings of the 2009 Third International Conference on Genetic and Evolutionary Computing
Year:
2009

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Bees Swarm Optimization for Web Association Rule Mining

WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03

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Abstract

Genetic algorithm is an important algorithm of association rule mining. However, there is some issues that genetic algorithm easy to lead prematuring convergence and into the plight of local optimum, or convergence too much time and consume a large amount of time to search. For resolving this issues, the paper improves the algorithm through adopting an adaptive mutation rate and improving the methods of individual choice, and the improved genetic algorithm that applies to the mining association rules. The simulating experiments show that the improved genetic algorithm reduces the cost of computing, and improve the efficiency of association rule mining.