Non Parametric Local Density-Based Clustering for Multimodal Overlapping Distributions

  • Damaris Pascual
  • Filiberto Pla
  • J. Salvador Sánchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Damaris Pascual
    • 1
  • Filiberto Pla
    • 2
  • J. Salvador Sánchez
    • 2
  1. 1.Dept de Ciencia de la ComputaciónUniversidad de OrienteSantiago de CubaCuba
  2. 2.Dept. Llenguatges i Sistemes InformàticsUniversitat Jaume ICastellóSpain

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