Abstract: The traditional fuzzy C-means(FCM) algorithm is sensitive to noise data and only considers the distance factor in the iterative process.Therefore,the use of Euclidean distance for distance measurement will result in only considering the local consistency feature between sample points,while ignoring the global consistency feature.To solve these problems,an improved FCM algorithm based on density sensitive distance and fuzzy partition is proposed.First,the density sensitive distance is used to replace the Euclidean distance in the calculation of the similarity matrix,and then fuzzy entropy is introduced as a constraint condition in the clustering process to derive the new clustering center and the membership calculation formula with Gaussian distribution characteristics.In addition,in view of the problem that the traditional FCM algorithm randomly selects the initial clustering center may cause the clustering result to be unstable,according to the two principles of denser sample points around the cluster center point and longer distance between the cluster center points,combined with the density sensitive distance to select the initial cluster center point.Finally,the experimental comparison proves that the improved algorithm not only has higher clustering perfor-mance and anti-noise,but also significantly improves the convergence speed compared with the traditional FCM clustering algorithm and its derivative algorithm.

Key words: Density sensitive distance, Fuzzy C-means clustering, Fuzzy entropy, Initial cluster center, Membership