DocumentCode :
3347685
Title :
An Algorithm for Mining Association Rules Based on Improved Genetic Algorithm and its Application
Author :
Guo, Hong ; Zhou, Ya
Author_Institution :
Sch. of Comput. & Control, Guilin Univ. of Electron. Technol., Guilin, China
fYear :
2009
fDate :
14-17 Oct. 2009
Firstpage :
117
Lastpage :
120
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.
Keywords :
data mining; genetic algorithms; adaptive mutation rate; association rule mining; improved genetic algorithm; individual choice methods; premature convergence; Association rules; Biological cells; Biology computing; Computational modeling; Data mining; Evolution (biology); Genetic algorithms; Genetic mutations; Itemsets; Transaction databases; Apriori; AssociationRule; Data Mining; Genetic Algorithm; PrematureConvergence;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Genetic and Evolutionary Computing, 2009. WGEC '09. 3rd International Conference on
Conference_Location :
Guilin
Print_ISBN :
978-0-7695-3899-0
Type :
conf
DOI :
10.1109/WGEC.2009.15
Filename :
5402932
Link To Document :
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