Grid-based clustering algorithm for muilti-density

被引：0

作者：

Qiu, BZ ^{[1
]}

Zhang, XZ ^{[1
]}

Shen, JY ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China

来源：

PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9 | 2005年

关键词：

density threshold descending; multi-stage clustering; border points extraction;

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：

This paper presents a grid-based clustering algorithm for multi-density (GDD). The GDD is a kind of the multi-stage clustering that integrates grid-based clustering, the technique of density threshold descending and border points extraction. Scanning the dataset only once, the GDD can discover clusters of arbitrary shapes. The experiment results show that it can discover outliers or noises effectively and get good cluster quality for multi-data sets.

引用

页码：1509 / 1512

页数：4

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