Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications

  • Authors:
  • Jörg Sander;Martin Ester;Hans-Peter Kriegel;Xiaowei Xu

  • Affiliations:
  • Institute for Computer Science, University of Munich, Oettingenstr. 67, D-80538 München, Germany. E-mail: sander@informatik.uni-muenchen.de, ester@informatik.uni-muenchen.de, kriegel@inform ...;Institute for Computer Science, University of Munich, Oettingenstr. 67, D-80538 München, Germany. E-mail: sander@informatik.uni-muenchen.de, ester@informatik.uni-muenchen.de, kriegel@inform ...;Institute for Computer Science, University of Munich, Oettingenstr. 67, D-80538 München, Germany. E-mail: sander@informatik.uni-muenchen.de, ester@informatik.uni-muenchen.de, kriegel@inform ...;Institute for Computer Science, University of Munich, Oettingenstr. 67, D-80538 München, Germany. E-mail: sander@informatik.uni-muenchen.de, ester@informatik.uni-muenchen.de, kriegel@inform ...

  • Venue:
  • Data Mining and Knowledge Discovery
  • Year:
  • 1998

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Abstract

The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well asto distinguish noise. In this paper, we generalize this algorithm in twoimportant directions. The generalized algorithm—calledGDBSCAN—can cluster point objects as well as spatially extended objects according to both, their spatial and their nonspatial attributes. Inaddition, four applications using 2D points (astronomy), 3D points(biology), 5D points (earth science) and 2D polygons (geography) arepresented, demonstrating the applicability of GDBSCAN to real-worldproblems.