Region-Restricted Clustering for Geographic Data Mining

  • Joachim Gudmundsson
  • Marc van Kreveld
  • Giri Narasimhan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4168)


Cluster detection for a set P of n points in geographic situations is usually dependent on land cover or another thematic map layer. This occurs for instance if the points of P can only occur in one land cover type. We extend the definition of clusters to region-restricted clusters, and give efficient algorithms for exact computation and approximation. The algorithm determines all axis-parallel squares with exactly m out of n points inside, size at most some prespepcified value, and area of a given land cover type at most another prespecified value. The exact algorithm runs in O(nmlog2 n + (nm+nn f )log2 n f ) time, where n f is the number of edges that bound the regions with the given land cover type. The approximation algorithm allows the square to be a factor 1+ε too large, and runs in O(n logn + n/ε 2 + n f log2 n f + (nlog2 n f )/( 2)) time. We also show how to compute largest clusters and outliers.


Land Cover Type Query Time Forest Inside Short Edge Point Pattern Analysis 
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 2006

Authors and Affiliations

  • Joachim Gudmundsson
    • 1
  • Marc van Kreveld
    • 2
  • Giri Narasimhan
    • 3
  1. 1.National ICT Australia Ltd.SydneyAustralia
  2. 2.Dept. of Computer ScienceUtrecht UniversityThe Netherlands
  3. 3.Florida International UniversityMiamiUSA

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