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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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 Type:||Article (Refereed Research - C1)|
© 2004 Elsevier : Reproduced in accordance with the copyright policy of the publisher : This journal is available online - use hypertext links above.
|Keywords:||Large spatial databases; Geographical Data Mining; Clustering; Delaunay triangulation; Association analysis|
|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%|
|Deposited On:||11 Sep 2006|
|Last Modified:||01 May 2013 09:22|
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|Citation Counts with External Providers:||Web of Science: 3|
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