Density-Based Distances: a New Approach for Evaluating Proximities Between Objects. Applications in Clustering and Discriminant Analysis.

The aim of this paper is twofold. First it is shown that taking densities between objects into account to define proximities between them is intuitively a right way to process. Secondly, some new distances based on density estimates are defined and some properties are presented. Many algorithms in clustering or discriminant analysis require the choice of a dissimilarity: two applications are presented, one in clustering and the other in discriminant analysis, and illustrate the benefits of using these new distances. [ABSTRACT FROM AUTHOR]