A New Cooperative Algorithm Based on PSO and K-Means for Data Clustering
- 1 ,
- 2 , Afganistan
Copyright: © 2020 Mehdi Neshat, Shima Farshchian Yazdi, Daneyal Yazdani and Mehdi Sargolzaei. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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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- Particle Swarm Optimization (PSO)
- Contraction Factor (CF-PSO)
- Sum of Intra cluster Distances (SISD)
- difference between Gbest fitness
- local optimum
- clustering algorithm