文献类型：会议论文

英文题名：A Graph Partition-based Soft Clustering Algorithm

作者：Chen Jianbin[1];Fang Deying[1];Shi Tong[1]

第一作者：陈建斌

通讯作者：Chen, JB[1]

机构：[1]Beijing Union Univ, Coll Business, Beijing 100025, Peoples R China

第一机构：北京联合大学商务学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Business, Beijing 100025, Peoples R China.|[1141721]北京联合大学商务学院;[11417]北京联合大学;

会议论文集：2nd International Symposium on Intelligent Information Technology Application

会议日期：DEC 21-22, 2008

会议地点：Shanghai, PEOPLES R CHINA

语种：英文

外文关键词：Cluster analysis - Fuzzy sets - Matrix algebra

摘要：Cluster analysis is one of the basic tools for discovering structure in data sets. Soft clustering enables the user to have a good overall view of the information contained in the data set that he has. However, existing soft algorithms suffer from various aspects. We propose GPSC (Graph Partition-based Soft clustering), an efficient soft clustering algorithm based on a given graph model. This algorithm projected data set to a graph firstly, applied graph partition method to get an initial clustering result. Secondly, the core vertices and verge vertices have been defined to measure the membership for each vertex to clusters and relationships of neighbor clusters. Then the membership matrix and relationship matrix have been induced From these two matrixes, we can find more fuzzy relations and latent clusters. Our experiments show that GPSC algorithm is able to discover clusters that cannot be detected by non-fuzzy algorithms, while maintaining a high degree of efficiency. Comparison with existing hard clustering algorithms like K-means and its variants shows that GPSC is both effective and efficient.