Data Mining and Knowledge Discovery

, Volume 2, Issue 2, pp 169–194

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

Authors

  • Jörg Sander
    • Institute for Computer ScienceUniversity of Munich
  • Martin Ester
    • Institute for Computer ScienceUniversity of Munich
  • Hans-Peter Kriegel
    • Institute for Computer ScienceUniversity of Munich
  • Xiaowei Xu
    • Institute for Computer ScienceUniversity of Munich
Article

DOI: 10.1023/A:1009745219419

Cite this article as:
Sander, J., Ester, M., Kriegel, H. et al. Data Mining and Knowledge Discovery (1998) 2: 169. doi:10.1023/A:1009745219419

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.

clustering algorithms spatial databases efficiency applications

Copyright information

© Kluwer Academic Publishers 1998