An new initialization method for fuzzy c-means algorithm

In this paper an initialization method for fuzzy c-means (FCM) algorithm is proposed in order to solve the two problems of clustering performance affected by initial cluster centers and lower computation speed for FCM. Grid and density are needed to extract approximate clustering center from sample space. Then, an initialization method for fuzzy c-means algorithm is proposed by using amount of approximate clustering centers to initialize classification number, and using approximate clustering centers to initialize initial clustering centers. Experiment shows that this method can improve clustering result and shorten clustering time validly.