Ludwig-Maximilians-Universität München, Institut für Informatik
Technical Report 95-08
- TITLE:
-
Knowledge Discovery in Large Spatial Databases: Focusing Techniques for
Efficient Class Identification
- 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:
-
knowledge discovery in databases, spatial query processing,
architecture of spatial database systems, 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 put special
emphasis on the integration of the discovery methods with the DB interface,
which is crucial for the efficiency of KDD on large databases. The key to this
integration is the use of a well-known spatial access method, the R*-tree. The
focusing component of a KDD system determines which parts of the database are
relevant for the knowledge discovery task. We present several strategies for
focusing: selecting representatives from a spatial database, focusing on the
relevant clusters and retrieving all objects of a given cluster. 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.
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