Clustering with obstacles for Geographical Data Mining

Estivill-Castro, Vladimir;Lee, Ickjai

Abstract

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.

Journal

ISPRS Journal of Photogrammetry and Remote Sensing

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ISBN/ISSN

1872-8235

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Issue

1-2

Pages Count

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Publisher

Elsevier

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EISSN

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DOI

10.1016/j.isprsjprs.2003.12.003