Improved Fuzzy C-means Clustering Algorithm Based on Sample Density

Fuzzy clustering techniques, especially fuzzy c-means (FCM) clustering algorithm, have been widely used in automated image segmentation. The performance of the FCM algorithm depends on the selection of initial cluster center and/or the initial memberships 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. In the paper for the problem that fuzzy c-means clustering algorithm is sensitive to the initial cluster centers, propose a method of selecting initial cluster centers based on sample density. At the end do experimental analysis and verification of the proposed key technologies. The results show that the proposed algorithm is superior to the FCM algorithms.