Non parametric local density-based clustering for multimodal overlapping distributions

Authors:
Damaris Pascual;Filiberto Pla;J. Salvador Sánchez
Affiliations:
Dept de Ciencia de la Computación, Universidad de Oriente, Santiago de Cuba, Cuba;Dept. Llenguatges i Sistemes Informàtics, Universitat Jaume I, Castelló, Spain;Dept. Llenguatges i Sistemes Informàtics, Universitat Jaume I, Castelló, Spain
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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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.