Optimization algorithm of K-means clustering center of selectionbased on density
基于密度的K-means聚类中心选取的优化算法

Aiming at the problem of traditional K-means algorithm which is sensitive to initial clustering center and the number of cluster, this paper proposed a kind of optimization algorithm of initial clustering center selection. The algorithm was accor-ding to the distribution density of data and calculated the two vertical halfway points recently to determine the initial clustering center, then combined the equalization function to optimize the cluster number and got the optimal cluster. Used the standard UCI data sets as the contrast experiment objects, and found that the improved algorithm has the high accuracy and relative stability compared with traditional algorithm.