DocumentCode :
2257739
Title :
A Novel GEP-Based Multiple-Layers Association Rule Mining Algorithm
Author :
Cai Hong-guo ; Yuan Chang-an ; Luo Jin-Guang ; Huang Jin-de
Author_Institution :
Dept. of Math. & Sci., Guangxi Coll. of Educ., Nanning, China
fYear :
2010
fDate :
11-14 Dec. 2010
Firstpage :
68
Lastpage :
72
Abstract :
To mine popular accessed Web pages items and find out their association rule from the Web server Log database for junior users providing recommendation service. A novel GEP-based algorithm for mining multiple-layers association rules was presented. Firstly, takes generalizing technology as a way to value fitness function in GEP (Gene Expression Programming). Then, relying on the significant self-search function of GEP, the most optional species was evolved. The frequent items and association rules in the next deeper layers can be mined by using traditional support-confidence method in sub-database. The algorithm improves on the frame of traditional association rule mining and uses a new evolutionary algorithm for mining association rules. Finally, the validity and efficiency of the method are presented by the application in the paper.
Keywords :
Internet; data mining; genetic algorithms; recommender systems; GEP-based algorithm; Web page; Web server log database; association rule mining; evolutionary algorithm; fitness function; gene expression programming; recommendation service; self search function; support confidence method; Abstract Frequency Items; Data mining; GEP; Generalizing; Multiple-layers association rule; Web Usage Mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Security (CIS), 2010 International Conference on
Conference_Location :
Nanning
Print_ISBN :
978-1-4244-9114-8
Electronic_ISBN :
978-0-7695-4297-3
Type :
conf
DOI :
10.1109/CIS.2010.22
Filename :
5696234
Link To Document :
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