Particle Swarm Optimization for Fuzzy c-Means Clustering

A new fuzzy c-means clustering algorithm based on particle swarm optimization (PSOFCM) is presented after analyzing the advantages and disadvantages of the classical fuzzy c-means clustering algorithm. It avoids the local optima, and also is robust to initialization. The fluctuation however has appeared in the new algorithm, so the improved PSOFCM has been proposed finally which has better convergence to lower quantization errors. We compared the performance of PSOFCM, improved PSOFCM and FCM with IRIS testing data. The experiments show that the performance of improved PSOFCM is far better than FCM and this is a viable and effective clustering algorithm