关键词: 高斯混合模型, 簇识别, 隶属度, 最大密度连通点

Abstract: In order to solve the problems that parameter settings depend on experience and low clustering precision in complex density environment in many clustering algorithms, this paper presents a fuzzy density clustering method by separating and combining basal density clusters based on the connected point with max density in sparse border. After calculating the density of data based on Gaussian mixture model, it buils a high dimensional discrete space of density, then transforms it into a high dimensional continuous space of density by using the low accuracy mesh to connect data and calculating the density of low accuracy null mesh with interpolation algorithm. After sorting the data according to the density, if a point can’t be found whose density is higher than the current point is next to the current point, it will be regarded as a maximum point, then class expansion is completed. With the help of contradiction during the expansion it can identify the connected point with max density in sparse border, and isolate the basal classes. On the basis of membership grade which can be calculated from the connected point with max density and maximum points, it completes the combination of basal classes and gets the final clusters. Finally, compared with a variety of density clustering algorithms, the simulation results verify that the algorithm reduces the dependence on experience parameters, the classification is more objective and efficient, the algorithm can plan with global unified, and it effectively improves the clustering accuracy.

Key words: Gaussian mixture model, recognition of clusters, membership grade, connected point with the max density