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