Robust Clustering of Large Geo-referenced Data Sets

Authors:
Vladimir Estivill-Castro;Michael E. Houle
Affiliations:
-;-
Venue:
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Year:
1999

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Abstract

Clustering geo-referenced data with the medoid method is related to k-Means, with the restriction that cluster representatives are chosen from the data. Although the medoid method in general produces clusters of high quality, it is often criticised for the Ω(n2) time that it requires. Our method incorporates both proximity and density information to achieve high-quality clusters in O(n log n) expected time. This is achieved by fast approximation to the medoid objective function using proximity information from Delaunay triangulations.