Spatial Data Mining: A Database Approach

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Spatial Data Mining: A Database Approach
Abstract. Knowledge discovery in databases (KDD) is an important task in spatial databases since both, the number and the size of such databases are rapidly growing. This paper introduces a set of basic operations which should be supported by a spatial database system (SDBS) to express algorithms for KDD in SDBS. For this purpose, we introduce the concepts of neighborhood graphs and paths and a small set of operations for their manipulation. We argue that these operations are sufficient for KDD algorithms considering spatial neighborhood relations by presenting the implementation of four typical spatial KDD algorithms based on the proposed operations. Furthermore, the efficient support of operations on large neighborhood graphs and on large sets of neighborhood paths by the SDBS is discussed. Neighborhood indices are introduced to materialize selected neighborhood graphs in order to speed up the processing of the proposed operations.
Martin Ester, Hans-Peter Kriegel, Jörg Sander
Added 08 Aug 2010
Updated 08 Aug 2010
Type Conference
Year 1997
Where SSD
Authors Martin Ester, Hans-Peter Kriegel, Jörg Sander
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