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...
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| Veröffentlicht in: | ISPRS journal of photogrammetry and remote sensing Jg. 59; H. 1; S. 21 - 34 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.08.2004
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| Schlagworte: | |
| ISSN: | 0924-2716, 1872-8235 |
| Online-Zugang: | Volltext |
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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. |
|---|---|
| AbstractList | 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. |
| Author | Lee, Ickjai Estivill-Castro, Vladimir |
| Author_xml | – sequence: 1 givenname: Vladimir surname: Estivill-Castro fullname: Estivill-Castro, Vladimir email: v.estivill-castro@griffith.edu.au organization: School of Computing and Information Technology, Nathan Campus, Griffith University, Brisbane 4111, QLD, Australia – sequence: 2 givenname: Ickjai surname: Lee fullname: Lee, Ickjai organization: School of Information Technology, James Cook University, Townsville 4181, QLD, Australia |
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| Cites_doi | 10.1109/T-C.1971.223083 10.1093/comjnl/41.8.578 10.1109/2.781637 10.1139/geomat-1991-0005 10.1016/0031-3203(94)00125-6 10.1145/568574.568575 10.1016/S0198-9715(01)00044-8 10.1080/136588198241734 10.1023/A:1015279009755 10.1007/BF01584648 10.1016/0734-189X(89)90146-1 10.1023/A:1016308404627 10.1080/02693798708927821 |
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| Keywords | delaunay triangulation clustering association analysis large spatial databases Geographical Data Mining |
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| SubjectTerms | association analysis clustering delaunay triangulation Geographical Data Mining large spatial databases |
| Title | Clustering with obstacles for Geographical Data Mining |
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