关键词: 聚类, 模糊C-均值, 密度函数, 马氏距离, 基于密度和马氏距离优化的模糊C-均值聚类（FCMBMD）算法

Abstract: In the light of the randomness of the initial clustering center selection and the limitations of distance vector formula application with the traditional Fuzzy C-Means clustering algorithm （FCM）, the optimized fuzzy C-means clustering algorithm （FCMBMD） is proposed. The algorithm is to determine the initial cluster center by computing the density of sample point, so it avoids the instability of clustering result generated randomly by initial cluster centers. In addition, it also meets the requirements of different units of measurement data using the similarity of Mahalanobis distance calculation sample set. The experimental result shows that FCMBMD algorithm has better effect in clustering center, convergence speed, iterations, accuracy, and so on.

Key words: clustering, Fuzzy C-Means（FCM）, density function, Mahalanobis distance, Fuzzy C-Means Based on Mahalanobis and Density（FCMBMD） algorithm