A Clustering Algorithm for Weighted Graph Based on Minimum Cut
Clustering is the unsupervised classification of patterns into groups.It groups a set of data in a way that maximizes the similarity within clusters and minimizes the similarity between two different clusters.In this paper,the Hartuv and Shamir’s clustering algorithm for similarity graph is extended to the weighted similarity graph.The algorithm has the advantage of many existing algorithm: low polynomial complexity,the provable properties,and automatically determining the number of clusters in the process of clustering.The algorithm is tested on random graph and the experimental results show that the algorithm performs well.
Chuangxin Yang Hong Peng Jiabing Wang
School of Computer Science and Engineering,South China University of Technology Guangzhou,510641,Chi School of Computer Science and Engineering,South China University of Technology Guangzhou,510641,Chi
国际会议
武汉
英文
2008-11-01(万方平台首次上网日期,不代表论文的发表时间)