@article {10.3844/jcssp.2012.188.194,
article_type = {journal},
title = {A New Cooperative Algorithm Based on PSO and K-Means for Data Clustering},
author = {Neshat, Mehdi and Yazdi, Shima Farshchian and Yazdani, Daneyal and Sargolzaei, Mehdi},
volume = {8},
number = {2},
year = {2011},
month = {Nov},
pages = {188-194},
doi = {10.3844/jcssp.2012.188.194},
url = {https://thescipub.com/abstract/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.
},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}