An efficient Fuzzy C-Means clustering algorithm

The Fuzzy C-Means (FCM) algorithm is commonly used for clustering. The performance of the FCM algorithm depends on the selection of the initial cluster center and/or the initial membership value. If a good initial cluster center that is close to the actual final cluster center can be found, the FCM algorithm will converge very quickly and the processing time can be drastically reduced. The authors propose a novel algorithm for efficient clustering. This algorithm is a modified FCM called the psFCM algorithm, which significantly reduces the computation time required to partition a dataset into desired clusters. We find the actual cluster center by using a simplified set of the original complete dataset. It refines the initial value of the FCM algorithm to speed up the convergence time. Our experiments show that the proposed psFCM algorithm is on average four times faster than the original FCM algorithm. We also demonstrate that the quality of the proposed psFCM algorithm is the same as the FCM algorithm