Ludwig-Maximilians-Universität München, Institut für Informatik
Technical Report 95-10
- TITLE:
-
A Database Interface for Clustering in Large Spatial Databases
- DATE:
-
May 1995
- AUTHORS:
- Martin Ester
- Hans-Peter Kriegel
- Xiaowei Xu
- {ester | kriegel | xwxu}@informatik.uni-muenchen.de
- Institut für Informatik
- Universität München
- Leopoldstr. 11B
- D-80802 München (Germany)
- KEYWORDS:
-
discovery algorithms for large databases, database interfaces,spatial
data sampling, clustering, application in molecular biology.
- ABSTRACT:
-
Both the number and the size of spatial databases are rapidly growing because
of the large amount of data obtained from satellite images, X-ray
crystallography or other scientific equipment. Therefore, automated knowledge
discovery becomes more and more important in spatial databases. So far, most of
the methods for knowledge discovery in databases (KDD) have been based on
relational database systems. In this paper, we address the task of class
identification in spatial databases using clustering techniques. We present an
interface to the database management system (DBMS), which is crucial for the
efficiency of KDD on large databases. This interface is based on a spatial
access method, the R*-tree. It clusters the objects according to their spatial
neighborhood and supports efficient processing of spatial queries. Furthermore,we propose a method for spatial data sampling as part of the focusing component,
significantly reducing the number of objects to be clustered. Thus, we achieve
a considerable speed-up for clustering in large databases. We have applied the
proposed techniques to real data from a large protein database used for
predicting protein-protein docking. A performance evaluation on this database
indicates that clustering on large spatial databases can be performed both
efficiently and effectively using our approach.
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