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An adaptive spatial clustering algorithm based on delaunay triangulation
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DOI：10.1016/j.compenvurbsys.2011.02.003.png)
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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.
Keyword:
Spatial clustering
Adaptive
Delaunay triangulation
Spatial data mining
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