文章基本信息

标题：Density Clustering Algorithm Based on Radius of Data (DCBRD)
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作者：Ahmed Fahim
期刊名称：Computer Sciences and Telecommunications
印刷版ISSN：1512-1232
出版年度：2006
期号：04
页码：32-43
出版社：Internet Academy
摘要：Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, a density based clustering algorithm is presented relying on a knowledge acquired from the data which is designed to discover clusters of arbitrary shape. The proposed algorithm requires no input parameter. We performed an experimental evaluation of the efficiency of it using real and synthetic data. The results of our experiments demonstrate that the proposed algorithm is significantly efficient in discovering clusters of arbitrary shape and size.