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.
Highlights
Data clustering has vast application in data categorization (Memarsadeghi and Leary, 2003),(Velmuruqan and Santhanam, 2010), data compression (Celebi, 2011), data mining (Pizzuti and Talia, 2003), pattern recognition (Wong and Li, 2008), compacting (Marr, 2003), machine learning (Yang et al, 2007), image segmentation (Vannoorenberghe and Flouzat, 2006) and Data clustering importance in various sciences causes the introduction of various methods of data clustering (Hartigan, 1975)
The k-means clustering algorithm was developed by Hartigan (1975) which is one of the earliest and simplest clustering approaches that has been ever which was presented by Kennedy and Eberhart (1995)
It has to be mentioned that the best obtained result from CF-Particle Swarm Optimization (PSO) is better than the proposed algorithm in three cases because this algorithm is of greater local search ability than k-means
Summary
Particle Swarm Optimization (PSO) is one multi-dimensional space, are grouped into some of the most famous swarm intelligence algorithms, clusters. The k-means clustering algorithm was developed by Hartigan (1975) which is one of the earliest and simplest clustering approaches that has been ever which was presented by Kennedy and Eberhart (1995). This algorithm is an effective technique for solving optimization problems that works based on probability rules and population. In KPSO, first, k-means method is executed and outcome of k-means is used as one of the particles in initial solution of PSO. Comparing obtained results from experiments shows an acceptable efficiency of the proposed algorithm
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