Clustering analysis method based on fuzzy C-means algorithm of PSO and PPSO with application in image data
Jeng-Ming Yih, Yuan‐Horng Lin, Hsiang-Chuan Liu · 2008
The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applying PSO to real-world applications is that PSO usually need a large number of fitness evaluations before a satisfying result can be obtained. In this paper, the improved new algorithm, Fuzzy C-Mean based on Picard iteration and PSO (PPSO-FCM), is proposed. Two real data sets were applied to prove that the performance of the PPSO-FCM algorithm is better than the conventional FCM algorithm and the PSO-FCM algorithm.