Determining the Convex Hull in Large Multidimensional Databases

  • Christian Böhm
  • Hans-Peter Kriegel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2114)


Determining the convex hull of a point set is a basic operation for many applications of pattern recognition, image processing, statistics, and data mining. Although the corresponding point sets are often large, the convex hull operation has not been considered much in a database context, and state-of-theart algorithms do not scale well to non main-memory resident data sets. In this paper, we propose two convex hull algorithms which are based on multidimensional index structures such as R-trees. One of them traverses the index depth-first. The other algorithm assigns a priority to each active node (nodes which are not yet accessed but known to the system), which corresponds to the maximum distance of the node region to the tentative convex hull. We show both theoretically as well as experimentally that our algorithms outperform competitive techniques that do not exploit indexes.


Convex Hull Online Algorithm Lower Left Corner Isotonic Regression Multidimensional Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Christian Böhm
    • 1
  • Hans-Peter Kriegel
    • 1
  1. 1.University of MunichMunichGermany

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