Robust distance-based clustering with applications to spatial data mining
In this paper we present a method for clustering gee-referenced data suitable for applications in spatial data mining, based on the medoid method. The medoid method is related to k-MEANS, With the restriction that cluster representatives be chosen from among the data elements. Although the medoid method in general produces clusters of high quality, especially in the presence of noise, it is often criticized for the Omega (n(2)) time that it requires. Our method incorporates both proximity and density information to achieve high-quality clusters in sub-quadratic time; it does not require that the user specify the number of clusters in advance. The time bound is achieved by means of a fast approximation to the medoid objective function, using Delaunay triangulations to store proximity information.
clustering data mining medoids noise delaunay triangulation spatial databases exploratory data analysis combinatorial optimization p-median problem