Small World Particle Swarm Optimizer for Data Clustering

  • Megha Vora
  • T. T. Mirnalinee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 259)


Particle swarm is a stochastic optimization paradigm inspired by the concepts of social psychology and artificial intelligence. Population topology plays significant role in the performance of PSO. It determines the way in which particles communicate and share information with each other. Topology can be depicted as a network model. Regular networks are highly clustered but the characteristic path length grows linearly with the increase in number of vertices. On the contrary, random networks are not highly clustered but they have small characteristic path length. Small world networks have a distinctive combination of regular and random networks i.e. highly clustered and small characteristic path length. This paper presents a novel algorithm for data clustering by incorporating the concept of small world in particle swarm optimization. Efficiency of the proposed methodology is tested by applying it on five standard benchmark data set. Results obtained are compared with another PSO variant. Comparative study demonstrates the effectiveness of the proposed approach.


Particle Swarm Optimization Particle Swarm Small World Small World Network Cluster Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  2. 2.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)Google Scholar
  3. 3.
    Mendes, R.: Population topologies and their influence in particle swarm performance. Ph.D. thesis, University of Minho (2004)Google Scholar
  4. 4.
    Milgram, S.: The small world problem. Psychol. Today 2, 60–67 (1967)Google Scholar
  5. 5.
    Travers, J., Milgram, S.: An experimental study of the small world problem. Sociometry 32, 425 (1969)CrossRefGoogle Scholar
  6. 6.
    Watts, D., Strogatz, S.: Collective dynamics of small-world networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  7. 7.
    Kleinberg, J.: The small-world phenomenon: an algorithmic perspective. Technical report, Cornell University Ithaca, NY (1999)Google Scholar
  8. 8.
    Saxena, A.K., Vora, M.: Novel approach for the use of small world theory in particle swarm optimization. In: 16th International Conference on Advanced Computing and Communications, pp. 363–366. IEEE (2008)Google Scholar
  9. 9.
    Pal, S.K., Ghosh, A., Uma Shankar, B.: Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation. Int. J. Remote Sens. 21(11), 2269–2300 (2000)CrossRefGoogle Scholar
  10. 10.
    Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI repository of machine learning databases. University of California, Irvine, Department of Information and Computer Sciences. (1998)
  11. 11.
    Jiang, B., Wang, N., Wang, L.: Particle swarm optimization with age-group topology for multimodal functions and data clustering. Commun. Nonlinear Sci. Numer. Simul. 18(11), 3134–3145 (2013)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Cura, T.: A particle swarm optimization approach to clustering. Expert Syst. Appl. 39(1), 1582–1588 (2012)CrossRefGoogle Scholar
  13. 13.
    Shelokar, P.: An ant colony approach to clustering. Anal. Chim. Acta 509(2), 187–195 (2004)CrossRefGoogle Scholar
  14. 14.
    Zhang, C., Ouyang, D., Ning, J.: An artificial bee colony approach for clustering. Expert Syst. Appl. 37(7), 4761–4767 (2010)CrossRefGoogle Scholar
  15. 15.
    Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 38(1), 218–237 (2008)CrossRefGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringS.S.N College of Engineering, Anna UniversityChennaiIndia

Personalised recommendations