关键词: 聚类, K-means算法, 复杂网络特征, 聚类初始点

Abstract: K-means algorithm is a partition-based clustering algorithm.It is simple and fast to converge，the performance of K-means algorithm depends on that how to choose K samples as the initial cluster centers.This paper develops the properties of complex network，and defines degree，congregated degree and congregated coefficient of objects with feature，and chooses the K nodes whose the degree and congregated coefficient are larger than the others as the initial cluster centers.The experiment shows that the improved K-means clustering algorithm is more efficient than the original K-means clustering algorithm.

Key words: clustering, K-means, complex network characteristics, initial cluster centers