Region-restricted clustering for geographic data mining

Authors:
Joachim Gudmundsson;Marc van Kreveld;Giri Narasimhan
Affiliations:
National ICT Australia Ltd., Sydney, Australia;Dept. of Computer Science, Utrecht University, The Netherlands;Florida International University, Miami
Venue:
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Year:
2006

Citing 13
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Abstract

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(nmlog2n + (nm+nnf)log2nf) time, where nf 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/ε2 + nflog2nf + (nlog2 nf)/(mε2)) time. We also show how to compute largest clusters and outliers.