DocumentCode :
1563912
Title :
A clustering algorithm with genetically optimized membership functions for fuzzy association rules mining
Author :
Kaya, Mehmet ; Alhajj, Reda
Author_Institution :
Dept. of Comput. Eng., Firat Univ., Elazig, Turkey
Volume :
2
fYear :
2003
Firstpage :
881
Abstract :
In this paper, we propose genetic algorithms (GAs) based clustering method, which dynamically adjusts the fuzzy sets to provide maximum profit within an interval of user specified minimum support values. This is achieved by tuning the base values of the membership functions for each quantitative attribute so as to maximize the sum of large itemsets in a certain interval of minimum support values. To the best of our knowledge, this is the first effort in this direction. To support our claim, we compare the proposed GAs-based approach with a CURE-based approach. Experimental results on synthetic transactions show that the proposed clustering method exhibits a good performance over CURE-based approach in terms of the number of produced large itemsets and interesting association rules.
Keywords :
data mining; fuzzy set theory; genetic algorithms; pattern clustering; CURE based approach; association rules; clustering algorithm; fuzzy association rules mining; fuzzy sets; genetic algorithms; genetically optimized membership functions; large itemsets; maximum profit; synthetic transactions; user specified minimum support values; Association rules; Clustering algorithms; Clustering methods; Data mining; Fuzzy sets; Fuzzy systems; Genetic algorithms; Genetic engineering; Humans; Itemsets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on
Print_ISBN :
0-7803-7810-5
Type :
conf
DOI :
10.1109/FUZZ.2003.1206547
Filename :
1206547
Link To Document :
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