%0 Journal Article
%A SHEN Yan
%A YU Donghua
%A WANG Haolei
%T Improvement of K-means based on particle swarm clustering algorithm
%D 2014
%R
%J Computer Engineering and Applications
%P 125-128
%V 50
%N 21
%X Combining particle swarm with K-means algorithm is one of the important methods in data mining, but all methods almost ignore the empty class problem which the particle update causes. This paper proposes a PK-means clustering algorithm based on multi-subswarms particle swarm and pseudo means, then is compared with both PSOK-means and K-means. The theory analysis and experiments show that the algorithm not only avoids empty clustering class but also has well global convergence and the local optimization, overcomes local minimum better, has a great effect on isolated data.
%U http://cea.ceaj.org/EN/abstract/article_32524.shtml