Non parametric local density-based clustering for multimodal overlapping distributions

Abstract

In this work, we present a clustering algorithm to find clusters of different sizes, shapes and densities, to deal with overlapping cluster distributions and background noise. The algorithm is divided in two stages. In a first step, local density is estimated at each data point. In a second stage, a hierarchical approach is used by merging clusters according to the introduced cluster distance, based on heuristic measures about how modes overlap in a distribution. Experimental results on synthetic and real databases show the validity of the method. © Springer-Verlag Berlin Heidelberg 2006.