Jörg Sander
Institute for Computer Science, University of Munich, Oettingenstr.
67, D-80538 München, Germany. E-mail:sander@informatik.uni-muenchen.de
Martin Ester
Institute for Computer Science, University of Munich, Oettingenstr.
67, D-80538 München, Germany. E-mail:ester@informatik.uni-muenchen.de
Hans-Peter Kriegel
Institute for Computer Science, University of Munich, Oettingenstr.
67, D-80538 München, Germany. E-mail:kriegel@informatik.uni-muenchen.de
Xiaowei Xu
Institute for Computer Science, University of Munich, Oettingenstr.
67, D-80538 München, Germany. E-mail:xwxu@informatik.uni-muenchen.de
Abstract
The clustering algorithm DBSCAN relies on a density-based notion of
clusters and is designed to discover clusters of arbitrary shape as well
as
to distinguish noise. In this paper, we generalize this algorithm in
two important directions. The generalized algorithm—called
GDBSCAN—can cluster point objects as well as spatially extended objects
according to both, their spatial and their nonspatial attributes. In
addition, four applications using 2D points (astronomy), 3D points
(biology), 5D points (earth science) and 2D polygons (geography) are
presented, demonstrating the applicability of GDBSCAN to real-world
problems.
Keywords
clustering algorithms, spatial databases, efficiency, applications
Article ID: 168713