A Software Tool for Data Clustering Using Particle Swarm Optimization

  • Kalyani Manda
  • A. Sai Hanuman
  • Suresh Chandra Satapathy
  • Vinaykumar Chaganti
  • A. Vinaya Babu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6466)


Many universities all over the world have been offering courses on swarm intelligence from 1990s. Particle Swarm Optimization is a swarm intelligence technique. It is relatively young, with a pronounce need for a mature teaching method. This paper presents an educational software tool in MATLAB to aid the teaching of PSO fundamentals and its applications to data clustering. This software offers the advantage of running the classical K-Means clustering algorithm and also provides facility to simulate hybridization of K-Means with PSO to explore better clustering performances. The graphical user interfaces are user-friendly and offer good learning scope to aspiring learners of PSO.


Particle swarm optimization data clustering learning tools 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bishop, X.M.: Neural networks for pattern recognition. Oxford University Press, Oxford (1995)MATHGoogle Scholar
  2. 2.
    Yurkiovich, S., Passino, K.M.: A laboratory course on fuzzy control. IEEE Trans. Educ. 42(1), 15–21 (1999)CrossRefGoogle Scholar
  3. 3.
    Coelho, L.S., Coelho, A.A.R.: Computational intelligence in process control: fuzzy, evolutionary, neural, and hybrid approaches. Int. J. Knowl-Based Intell. Eng. Sys. 2(2), 80–94 (1998)Google Scholar
  4. 4.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence: from natural to artificial systems. Oxford University Press, Oxford (1999)MATHGoogle Scholar
  5. 5.
    Kennedy, J.F., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International conference on neural networks, Perth, Australia, vol. 4, pp. 1942–1948 (1995)Google Scholar
  6. 6.
    MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press (1967)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kalyani Manda
    • 1
  • A. Sai Hanuman
    • 2
  • Suresh Chandra Satapathy
    • 3
  • Vinaykumar Chaganti
    • 4
  • A. Vinaya Babu
    • 5
  1. 1.Maharaj Vijayaram Gajapat Raj Engineering CollegeVijayanagaramIndia
  2. 2.GRIETHyderabadIndia
  3. 3.Anil Neerukonda Institute of Technology and ScienceVisakhapatnamIndia
  4. 4.GITAMVishakapatnamIndia
  5. 5.JNTUHyderabadIndia

Personalised recommendations