Research of Grid-Similarity-based Clustering Algorithm

摘要：

Aim at the limitations of traditional measurement method on similitude between objects, we put forward Grid-Similarity-based Clustering Algorithm (GSCA), it brings in a new criterion to measure the similitude between objects. It applies on the grid clustering and disposes the density threshold of grid by the method of density threshold that improves the precision of clustering. Besides, the GSCA algorithm disposes the very high dimension datasets by the technique of entropy. The algorithm appears its advantages in the comparative experiments with some traditional clustering algorithm.

关键词： grid similarity threshold entropy

作者: Chun-jiang pang

作者单位: College of Computer Science and Technic, North China Electric Power University, Baoding, Hebei 071003, China

会议类型: 国际会议

会议名称: 2009 WASE International Conference on Information Engineering(2009年国际信息工程会议)(ICIE 2009)

会议地点: 太原

会议语种:英文

页码: 692-695

在线出版日期: 2009-07-10（万方平台首次上网日期，不代表论文的发表时间）