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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:ISPRS journal of photogrammetry and remote sensing Ročník 59; číslo 1; s. 21 - 34
Hlavní autoři: Estivill-Castro, Vladimir, Lee, Ickjai
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.08.2004
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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