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A Novel Clustering Algorithm based on Particle Swarm Optimization

Xiaojun Wu


K-means clustering algorithm is widely used in the clustering analysis. It takes the mean value of each cluster center as the Heuristic information, so it has high computation efficiency. But it also has limitations: sensitive to the initial center and easy to fall into local optimum. Particle Swarm Optimization has the characteristics of Parallelism and distribution in searching which is convenient in processing massive data. Because of its  good ability in global searching, it can overcome the disadvantages of K-means algorithm. By improving PSO algorithm, it can enhance the searching probability around the optimal solutions and reduce the sensitivity to the initialization. The combination of the improved PSO algorithm and K-means can improve the stability and convergence efficiency. Experimental results on four UCI data  sets show that the novel algorithm has the ability to identify clusters more efficiently.

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