An Efficient Grid Cell Based Spatial Clustering Algorithm for Spatial Data Mining


The KIPS Transactions:PartD, Vol. 10, No. 4, pp. 567-576, Aug. 2003
10.3745/KIPSTD.2003.10.4.567,   PDF Download:

Abstract

Spatial data mining, i.e., discovery of interesting characteristics and patterns that may implicitly exists in spatial databases, is a challenging task due to the huge amounts of spatial data. Clustering algorithms are attractive for the task of class identification in spatial databases. Several methods for spatial clustering have been presented in recent years, but have the following several drawbacks : increase costs due to computing distance among objects and process only memory-resident data. In this paper, we propose an efficient grid cell based spatial clustering method for spatial data mining. It focuses on resolving disadvantages of existing clustering algorithms. In details, it aims to reduce cost further for good efficiency on large databases. To do this, we devise a spatial clustering algorithm based on grid cell structures including cell relationships.


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Cite this article
[IEEE Style]
M. S. Ho, L. D. Gyu, S. Y. Deog, "An Efficient Grid Cell Based Spatial Clustering Algorithm for Spatial Data Mining," The KIPS Transactions:PartD, vol. 10, no. 4, pp. 567-576, 2003. DOI: 10.3745/KIPSTD.2003.10.4.567.

[ACM Style]
Mun Sang Ho, Lee Dong Gyu, and Seo Yeong Deog. 2003. An Efficient Grid Cell Based Spatial Clustering Algorithm for Spatial Data Mining. The KIPS Transactions:PartD, 10, 4, (2003), 567-576. DOI: 10.3745/KIPSTD.2003.10.4.567.