An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through...

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Published in:	ISPRS international journal of geo-information Vol. 6; no. 9; p. 272
Main Authors:	Zhan, Qingming, Deng, Shuguang, Zheng, Zhihua
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.09.2017
Subjects:	Adaptive algorithms Algorithms Clustering Clusters Data data collection data stream Data transmission Delaunay triangulation Dynamic characteristics Economic models Gestalt theory Mathematical models spatial clustering Spatial data sweep-circle trees Voronoi graphs
ISSN:	2220-9964, 2220-9964
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Abstract	An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram). It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.
AbstractList	An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram). It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.
Author	Zheng, Zhihua Deng, Shuguang Zhan, Qingming
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SubjectTerms	Adaptive algorithms Algorithms Clustering Clusters Data data collection data stream Data transmission Delaunay triangulation Dynamic characteristics Economic models Gestalt theory Mathematical models spatial clustering Spatial data sweep-circle trees Voronoi graphs
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Title	An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt
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