CHSMST: A CLUSTERING ALGORITHM BASED ON HYPER SURFACE AND MINIMUM SPANNING TREE

摘要：

Firstly, a new Clustering algorithm based on Hyper Surface (CHS) is put forward in this paper. CHS needs no domain knowledge to determine input parameters. However, it is difficult to process locally dense data for CHS. Then, an efficient clustering algorithm CHSMST is proposed, which is based on CHS and Minimum Spanning Tree. In the first step, CHSMST applies CHS to obtain initial clusters. After interacting, minimum spanning tree is introduced to handle locally dense data with which it is hard for CHS to deal. The experiments show that CHSMST can discover clusters with arbitrary shape. Moreover, the run time of CHSMST increases moderately as the scale of data set becomes large.

关键词： Hyper surface classification Clustering based on hyper surface Minimum spanning tree Clustering algorithm Data mining

作者: QING HE WEI-ZHONG ZHAO ZHONG-ZHI SHI

作者单位: The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 2657-2662

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）