Research Article Open Access

A New Cooperative Algorithm Based on PSO and K-Means for Data Clustering

Mehdi Neshat1, Shima Farshchian Yazdi2, Daneyal Yazdani1 and Mehdi Sargolzaei1
  • 1 ,
  • 2 , Afganistan
Journal of Computer Science
Volume 8 No. 2, 2012, 188-194

DOI: https://doi.org/10.3844/jcssp.2012.188.194

Submitted On: 8 October 2011 Published On: 19 November 2011

How to Cite: Neshat, M., Yazdi, S. F., Yazdani, D. & Sargolzaei, M. (2012). A New Cooperative Algorithm Based on PSO and K-Means for Data Clustering. Journal of Computer Science, 8(2), 188-194. https://doi.org/10.3844/jcssp.2012.188.194

Abstract

Problem statement: Data clustering has been applied in multiple fields such as machine learning, data mining, wireless sensor networks and pattern recognition. One of the most famous clustering approaches is K-means which effectively has been used in many clustering problems, but this algorithm has some drawbacks such as local optimal convergence and sensitivity to initial points. Approach: Particle Swarm Optimization (PSO) algorithm is one of the swarm intelligence algorithms, which is applied in determining the optimal cluster centers. In this study, a cooperative algorithm based on PSO and k-means is presented. Result: The proposed algorithm utilizes both global search ability of PSO and local search ability of k-means. The proposed algorithm and also PSO, PSO with Contraction Factor (CF-PSO), k-means algorithms and KPSO hybrid algorithm have been used for clustering six datasets and their efficiencies are compared with each other. Conclusion: Experimental results show that the proposed algorithm has an acceptable efficiency and robustness.

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Keywords

  • Particle Swarm Optimization (PSO)
  • Contraction Factor (CF-PSO)
  • Sum of Intra cluster Distances (SISD)
  • difference between Gbest fitness
  • local optimum
  • clustering algorithm