DocumentCode :
457105
Title :
Multi-Objective Evolutionary Clustering using Variable-Length Real Jumping Genes Genetic Algorithm
Author :
Ripon, Kazi Shah Nawaz ; Tsang, Chi-Ho ; Kwong, Sam ; Ip, Man-Ki
Author_Institution :
Dept. of Comput. Sci., Hong Kong City Univ.
Volume :
1
fYear :
0
fDate :
0-0 0
Firstpage :
1200
Lastpage :
1203
Abstract :
In this paper, we present a novel multi-objective evolutionary clustering approach using variable-length real jumping genes genetic algorithms (VRJGGA). The proposed algorithm that extends jumping genes genetic algorithm (JGGA) (Man et al., 2004) evolves near-optimal clustering solutions using multiple clustering criteria, without a-priori knowledge of the actual number of clusters. Experimental results based on several artificial and real-world data show that VRJGGA can obtain non-dominated and near-optimal clustering solutions in terms of different cluster quality measures and classification performance
Keywords :
genetic algorithms; pattern classification; pattern clustering; cluster quality measure; multiobjective evolutionary clustering; multiple clustering criteria; near-optimal clustering; variable-length real jumping genes genetic algorithm; Biological cells; Clustering algorithms; Clustering methods; Computer science; Encoding; Evolutionary computation; Flowcharts; Genetic algorithms; Genetic mutations; Poles and towers;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
ISSN :
1051-4651
Print_ISBN :
0-7695-2521-0
Type :
conf
DOI :
10.1109/ICPR.2006.827
Filename :
1699105
Link To Document :
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