An Improved Cat Swarm Optimization Algorithm for Clustering

  • Yugal KumarEmail author
  • G. Sahoo
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)


Clustering is an efficient technique that can be put in place to find out some sort of relationship in the data. Large number of heuristic approaches have been used for clustering task. The Cat Swarm Optimization (CSO) is the latest meta-heuristic algorithm which has been applied in clustering field and provided better results than K-Means and Particle Swarm Optimization (PSO). However, this algorithm is suffered with diversity problem. To overcome this problem, an improved version of CSO method using Cauchy mutation operator is proposed. The performance of improved CSO is compared with the existing methods like K-Means, PSO and CSO on several artificial and real datasets. From the simulation study, it came to revelation that the improved CSO algorithm gives better quality solution than others.


Cat swarm optimization Cauchy mutation operator Clustering and particle swarm optimization 


  1. 1.
    MacQueen, J.: On convergence of k-means and partitions with minimum average variance. Ann. Math. Statist 36, 1084 (1965)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recog. Lett. 31(8), 651–666 (2010)CrossRefGoogle Scholar
  3. 3.
    Bandyopadhyay, S., Maulik, U.: Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recogn. 35(6), 1197–1208 (2002)CrossRefzbMATHGoogle Scholar
  4. 4.
    Krishna, K., Narasimha, M.: Murty: genetic K-means algorithm. IEEE Trans. Syst. Man Cybern. B Cybern. 29(3), 433–439 (1999)CrossRefGoogle Scholar
  5. 5.
    Selim, S.Z., Alsultan, K.L.: A simulated annealing algorithm for the clustering problem. Pattern Recogn. 24(10), 1003–1008 (1991)Google Scholar
  6. 6.
    Maulik, U., Mukhopadhyay, A.: Simulated annealing based automatic fuzzy clustering combined with ANN classification for analyzing microarray data. Comput. Oper. Res. 37(8), 1369–1380 (2010)Google Scholar
  7. 7.
    Sung, C.S., Jin, H.W.: A tabu-search-based heuristic for clustering. Pattern Recogn. 33(5), 849–858 (2000)Google Scholar
  8. 8.
    Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: An ant colony approach for clustering. Anal. Chim. Acta 509(2), 187–195 (2004)CrossRefGoogle Scholar
  9. 9.
    Kao, Y.T., Zahara, E., Kao, I.W.: A hybridized approach to data clustering. Expert Syst. Appl. 34(3), 1754–1762 (2008)CrossRefGoogle Scholar
  10. 10.
    Zhang, Changsheng, Ouyang, Dantong, Ning, Jiaxu: An artificial bee colony approach for clustering. Expert Syst. Appl. 37(7), 4761–4767 (2010)CrossRefGoogle Scholar
  11. 11.
    Kumar, Y., Sahoo, G.: A charged system search approach for data clustering. Prog. Artif. Intell. 2, 153–166 (2014)Google Scholar
  12. 12.
    Kumar, Y., Sahoo, G.: A chaotic charged system search approach for data clustering. Informatica (accepted, in press)Google Scholar
  13. 13.
    Satapathy, S.C., Naik, A.: Data clustering based on teaching-learning-based optimization. In: Swarm, Evolutionary, and Memetic Computing, pp. 148–156 (2011)Google Scholar
  14. 14.
    Sahoo, A.J., Kumar, Y.: Improved teacher learning based optimization method for data clustering. In Advances in Signal Processing and Intelligent Recognition Systems, pp. 429–437. Springer International Publishing, Heidelberg (2014)Google Scholar
  15. 15.
    Chu, S.C., Tsai, P.W., Pan, J.S..: Cat swarm optimization. In: PRICAI 2006: Trends in Artificial Intelligence, pp. 854–858. Springer, Heidelberg (2006)Google Scholar
  16. 16.
    Santosa, B., Ningrum, M.K.: Cat swarm optimization for clustering. In: International Conference of Soft Computing and Pattern Recognition, SOCPAR’09, IEEE, 54–59 (2009)Google Scholar
  17. 17.
    Hu, X., Eberhart, R. C., and Shi, Y.: Swarm intelligence for permutation optimization: a case study on n-queens problem. In: Proceedings of IEEE Swarm Intelligence Symposium, pp. 243–246 (2003)Google Scholar
  18. 18.
    Yao, Xin, Liu, Yong, Lin, Guangming: Evolutionary programming made faster. Evol. Comput. IEEE Trans. 3(2), 82–102 (1999)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringBirla Institute of TechnologyMesra, RanchiIndia

Personalised recommendations