DBRS: A density-based spatial clustering method with random sampling

被引：0

作者：

Wang, X ^{[1
]}

Hamilton, HJ ^{[1
]}

机构：

[1] Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada

来源：

ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING | 2003年 / 2637卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a novel density-based spatial clustering method called DBRS. The algorithm can identify clusters of widely varying shapes, clusters of varying densities, clusters which depend on non-spatial attributes, and approximate clusters in very large databases. DBRS achieves these results by repeatedly picking an unclassified point at random and examining its neighborhood. A theoretical comparison of DBRS and DBSCAN, a well-known density-based algorithm, is also given in the paper.

引用

页码：563 / 575

页数：13

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