A Fuzzy Similarity-based Clustering Optimized by Particle Swarm Optimization

Traditional fuzzy clustering algorithms based on objective function is unable to determine the optimum number of clusters, sensitive to the initial cluster centers, and easily sunk into the issue of local optimum. A Fuzzy similarity-based clustering (FSBC) algorithm is proposed in this paper. This method consists three phases: first, the objective function is modified by integrating Fuzzy C-means (FCM) and Possibilistic C-means (PCM) method; second, using the density function from data for similarity-based clustering to automatically generate initial prototype without requesting users to specify; finally, the iteration process optimized by Particle swarm optimization (PSO) to obtain appropriate adjustment parameters that can provide better results, which avoids the local minimum problems of traditional methods. The experimental results on the synthetic data and UCI standard data sets show that the proposed algorithm has greater searching capability, less computational complexity, higher clustering precision.