18th International Conference on Pattern Recognition (ICPR'06) Volume 1 Multi-Objective Evolutionary Clustering using Variable-Length Real Jumping Genes Genetic Algorithm Hong Kong August 20-August 24 ISBN: 0-7695-2521-0
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.827
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) [1] evolves near-optimal clustering solutions using multiple clustering criteria, without apriori knowledge of the actual number of clusters. Experimental results based on several artificial and realworld data show that VRJGGA can obtain non-dominated and near-optimal clustering solutions in terms of different cluster quality measures and classification performance.
Citation:
Kazi Shah Nawaz Ripon, Chi-Ho Tsang, Sam Kwong, Man-Ki Ip, "Multi-Objective Evolutionary Clustering using Variable-Length Real Jumping Genes Genetic Algorithm," icpr, vol. 1, pp.1200-1203, 18th International Conference on Pattern Recognition (ICPR'06) Volume 1, 2006 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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