Clustering with obstacles for Geographical Data Mining

Estivill-Castro, Vladimir, and Lee, Ickjai (2004) Clustering with obstacles for Geographical Data Mining. ISPRS Journal of Photogrammetry and Remote Sensing, 59 (1-2). pp. 21-34.

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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.

Item ID: 299
Item Type: Article (Refereed Research - C1)
Keywords: Large spatial databases; Geographical Data Mining; Clustering; Delaunay triangulation; Association analysis
Additional Information:

© 2004 Elsevier : Reproduced in accordance with the copyright policy of the publisher : This journal is available online - use hypertext links above.

ISSN: 1872-8235
Date Deposited: 11 Sep 2006
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0804 Data Format > 080403 Data Structures @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8999 Other Information and Communication Services > 899999 Information and Communication Services not elsewhere classified @ 100%
Citation Count from Web of Science Web of Science 5
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