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IJMLC 2012 Vol.2(6): 839-843 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.249

Integrated PSO and DE for Data Clustering

Suresh Satapathy, Divya Maheshwari, Sai Hanuman A, Vinaya Babu A, P. K. Patra, and B. N. Biswal

Abstract—This paper studies the applicability of hybridization of Differential Evolution (DE) and PSO techniques to data clustering problem. A new way of integrating DE and PSO is explored in the paper. In one approach, a parallel DE and PSO developed and in other, a transitional approach of alternate DE and PSO technique followed. Simulations for number of data sets show that the proposed integrated approach provides better clustering performance.

Index Terms—Clustering, PSO, differential evolution.

The authors are with the Department of CSE, ANITS, Visakhapatnam, AP, India(e-mail: sureshsatapathy@ieee.org; sureshsatapathy@ieee.org).

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Cite:Suresh Satapathy, Divya Maheshwari, Sai Hanuman A, Vinaya Babu A, P. K. Patra, and B. N. Biswal, "Integrated PSO and DE for Data Clustering," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 839-843, 2012.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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