An adaptive spatial clustering algorithm based on delaunay triangulation

被引：126

作者：

Deng, Min ^{[1
]}

Liu, Qiliang ^{[1
]}

Cheng, Tao ^{[2
]}

Shi, Yan ^{[1
]}

机构：

[1] Cent S Univ, Dept Surveying & Geoinformat, Changsha, Hunan, Peoples R China

[2] UCL, Dept Civil Environm & Geomat Engn, London WC1E 6BT, England

来源：

COMPUTERS ENVIRONMENT AND URBAN SYSTEMS | 2011年 / 35卷 / 04期

基金：

国家高技术研究发展计划(863计划); 中国国家自然科学基金;

关键词：

Spatial clustering; Adaptive; Delaunay triangulation; Spatial data mining;

D O I：

10.1016/j.compenvurbsys.2011.02.003

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, an adaptive spatial clustering algorithm based on Delaunay triangulation (ASCDT for short) is proposed. The ASCDT algorithm employs both statistical features of the edges of Delaunay triangulation and a novel spatial proximity definition based upon Delaunay triangulation to detect spatial clusters. Normally, this algorithm can automatically discover clusters of complicated shapes, and non-homogeneous densities in a spatial database, without the need to set parameters or prior knowledge. The user can also modify the parameter to fit with special applications. In addition, the algorithm is robust to noise. Experiments on both simulated and real-world spatial databases (i.e. an earthquake dataset in China) are utilized to demonstrate the effectiveness and advantages of the ASCDT algorithm. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

引用

页码：320 / 332

页数：13

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