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TKDE
2002

CLARANS: A Method for Clustering Objects for Spatial Data Mining

8 years 8 months ago
CLARANS: A Method for Clustering Objects for Spatial Data Mining
Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. To this end, this paper has three main contributions. First, we propose a new clustering method called CLARANS, whose aim is to identify spatial structures that may be present in the data. Experimental results indicate that, when compared with existing clustering methods, CLARANS is very efficient and effective. Second, we investigate how CLARANS can handle not only points objects, but also polygon objects efficiently. One of the methods considered, called the IR-approximation, is very efficient in clustering convex and nonconvex polygon objects. Third, building on top of CLARANS, we develop two spatial data mining algorithms that aim to discover relationships between spatial and nonspatial attributes. Both algorithms can discover knowledge that is difficult to find with existing spatial data mining algorithms.
Raymond T. Ng, Jiawei Han
Added 23 Dec 2010
Updated 23 Dec 2010
Type Journal
Year 2002
Where TKDE
Authors Raymond T. Ng, Jiawei Han
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