✦ LIBER ✦

Clustering with obstacles for Geographical Data Mining

✍ Scribed by Vladimir Estivill-Castro; Ickjai Lee

Publisher: Elsevier Science
Year: 2004
Tongue: English
Weight: 381 KB
Volume: 59
Category: Article
ISSN: 0924-2716
DOI: 10.1016/j.isprsjprs.2003.12.003

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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