Abstract：The global K - means clustering algorithm needs to randomly select the initial clustering center. In this paper, the K -medoids is used as the initial clustering center, and the global K - means clustering algorithm is improved. With the data analysis in the database, the performances of the two algorithms are compared. It concludes that the clustering time of the improved algorithm is short and its robustness is strong .
李燕梅. 一种基于全局K-均值聚类的改进算法[J]. 电脑与电信, .
LI Yan-mei. An Improvement Algorithm Based on Global K- means Clustering. Computer & Telecommunication, 2017, 1(11): 25-27.
