Clustering with obstacles for Geographical Data Mining

Clustering algorithms typically use the Euclidean distance. However, spatial proximity is dependent on obstacles, caused by related information in other layers of the spatial database. We present a clustering algorithm suitable for large spatial databases with obstacles. The algorithm is free of use...

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Bibliographic Details
Published in:ISPRS journal of photogrammetry and remote sensing Vol. 59; no. 1; pp. 21 - 34
Main Authors: Estivill-Castro, Vladimir, Lee, Ickjai
Format: Journal Article
Language:English
Published: Elsevier B.V 01.08.2004
Subjects:
association analysis
clustering
delaunay triangulation
Geographical Data Mining
large spatial databases
delaunay triangulation
clustering
association analysis
large spatial databases
Geographical Data Mining
ISSN:0924-2716, 1872-8235
Online Access:Get full text
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Summary:Clustering algorithms typically use the Euclidean distance. However, spatial proximity is dependent on obstacles, caused by related information in other layers of the spatial database. We present a clustering algorithm suitable for large spatial databases with obstacles. The algorithm is free of user-supplied arguments and incorporates global and local variations. The algorithm detects clusters in complex scenarios and successfully supports association analysis between layers. All this occurs within O( n log n+[ s+ t] log n) expected time, where n is the number of points, s is the number of line segments that determine the obstacles and t is the number of Delaunay edges intersecting the obstacles.
ISSN:0924-2716
1872-8235
DOI:10.1016/j.isprsjprs.2003.12.003