An Improved K-means Clustering Algorithm for Complex Networks

被引:0
|
作者
Li, Hao [1 ]
Wang, Haoxiang [1 ]
Chen, Zengxian [1 ]
机构
[1] S China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Guangdong, Peoples R China
关键词
Complex Networks; K-means; Clustering; Node Importance; COMMUNITY STRUCTURE;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The exploration about cluster structure in Complex Networks is crucial for analyzing and understanding Complex Networks. K-means algorithm is a widely used clustering algorithm. In this paper, a novel algorithm is proposed based on K-means. Considering, Complex Networks obeys Power-law Degree Distribution, this improved algorithm chooses nodes with high importance as the initial clustering centroids, and uses the distance to these key nodes as clustering measurement. The experiments prove that the new algorithm can conduct accurate clustering with acceptable performance.
引用
收藏
页码:90 / 93
页数:4
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