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Soft Rank Clustering

  • Stefano Rovetta
  • Francesco Masulli
  • Maurizio Filippone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3931)

Abstract

Clustering methods provide an useful tool to tackle the problem of exploring large-dimensional data. However many common approaches suffer from being applied in high-dimensional spaces. Building on a dissimilarity-based representation of data, we propose a dimensionality reduction technique which preserves the clustering structure of the data. The technique is designed for cases in which data dimensionality is large compared to the number of available observations. In these cases, we represent data in the space of soft D-ranks, by applying the concept of fuzzy ranking. A clustering procedure is then applied. Experimental results show that the method is able to retain the necessary information, while considerably reducing dimensionality.

Keywords

Cluster Algorithm Linkage Method Dissimilarity Matrix Dimensionality Reduction Technique Neighbor Linkage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stefano Rovetta
    • 1
    • 2
  • Francesco Masulli
    • 2
    • 3
  • Maurizio Filippone
    • 1
    • 2
  1. 1.Dipartimento di Informatica e Scienze dell’InformazioneUniversità di GenovaGenovaItaly
  2. 2.Unità di GenovaIstituto Nazionale per la Fisica della MateriaGenovaItaly
  3. 3.Dipartimento di InformaticaUniversità di PisaPisaItaly

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