A LOCATION-OPTIMIZED CLUSTERING ALGORITHM BASED ON HIERARCHIES AND DENSITY

摘要：

A new kind of clustering algorithm called LOCAHID is presented in this paper. LOCAHID views each potential cluster as a tight coupling structure, which can be described by a density tree. Every density tree is dynamically generated according to its local density distribution. Those closerdusters are merged if some conditions are satisfied. In order to extend its applications to large data sets, a typical localtion-optimized technology is introduced to lower its running time and space storages. LOCAHID inherits the strongpoints of hierarchical methods and density-based methods, such as preferable accuracy in discovering clusters with arbitrary shape, good ability of processing noise data sets,weak sensitivity to input parameters and no limitation of global density threshold. The experiments illustrate the effectiveness.

关键词： Data mining clustering algorithm hierarchy CABDET

作者: WEI-DI DAI PI-LIAN HE HONG-LEI ZHU JIE LIU TONG WANG

作者单位: Department of Computer Science and Technology, Tianjin University, Tianjin 300072, China Academic Affairs Office, TianJin University, Tianjin 300072, China The Vocational Technique Instruction College, TianJin University, Tianjin 300072, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 1216-1220

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）