TY - GEN
T1 - Multi-objective evolutionary clustering using variable-length real jumping genes genetic algorithm
AU - RIPON, Kazi Shah Nawaz
AU - TSANG, Chi-Ho
AU - KWONG, Sam
AU - IP, Man-Ki
PY - 2006
Y1 - 2006
N2 - 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 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. © 2006 IEEE.
AB - 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 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. © 2006 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=34047205931&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2006.827
DO - 10.1109/ICPR.2006.827
M3 - Conference paper (refereed)
SP - 1200
EP - 1203
BT - Proceedings - International Conference on Pattern Recognition
ER -