Scientific Research and Essays

  • Abbreviation: Sci. Res. Essays
  • Language: English
  • ISSN: 1992-2248
  • DOI: 10.5897/SRE
  • Start Year: 2006
  • Published Articles: 2768

Full Length Research Paper

Cat swarm optimization clustering (KSACSOC): A cat swarm optimization clustering algorithm

Yongguo Liu1,2,3*, Xindong Wu3 and Yidong Shen2
1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China. 2State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100191, P. R. China. 3Department of Computer Science, University of Vermont, Burlington, Vermont 05405, USA.
Email: [email protected]

  •  Accepted: 15 November 2012
  •  Published: 17 December 2012


Clustering is an unsupervised process that divides a given set of objects into groups so that objects within a cluster are highly similar with one another and dissimilar with the objects in other clusters. In this article, a new clustering method based on cat swarm optimization was proposed to find the proper clustering of data sets called K-means improvement and Simulated Annealing selection based cat swarm optimization clustering (KSACSOC). In the KSACSOC method, the seeking mode with k-means improvement was designed to enhance the clustering solution obtained in the process of iterations, and the tracing mode with simulated annealing selection was developed to explore the unvisited solution space. Experimental results on two artificial and six real life data sets are given to illustrate the superiority of the proposed algorithm over k-means algorithm, a simulated annealing clustering method, and a particle swarm optimization clustering method.


Key words: Clustering, cat swarm optimization, k-means, simulated annealing.