Martin Ester,Hans-Peter Kriegel,Jörg Sander,Xiaowei Xu Erschienen:
KI 1/98
Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu
In the past few decades, clustering has been widely used in areas such
as pattern recognition, data analysis, and image processing. Recently,
clustering has been recognized as a primary data mining method for
knowledge discovery in spatial databases, i.e. databases managing 2D or
3D points, polygons etc. or points in some d-dimensional feature space.
The well-known clustering algorithms, however, have some
drawbacks when applied to large spatial databases. First, they assume
that all objects to be clustered reside in main memory. Second, these
methods are too inefficient when applied to large databases. To overcome
these limitations, new algorithms have been developed which are
surveyed in this paper. These algorithms make use of efficient query
processing techniques provided by spatial database systems.