Robust Clusterin of Large Geo-referenced Data Sets

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(n 2 ) 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.