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NEC: A Hierarchical Agglomerative Clustering Based on Fisher and Negentropy Information

  • Angelo Ciaramella
  • Giuseppe Longo
  • Antonino Staiano
  • Roberto Tagliaferri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3931)

Abstract

In this paper a hierarchical agglomerative clustering is introduced. A hierarchy of two unsupervised clustering algorithms is considered. The first algorithm is based on a competitive Neural Network or on a Probabilistic Principal Surfaces approach and the second one on an agglomerative clustering based on both Fisher and Negentropy information. Different definitions of Negentropy information are used and some tests on complex synthetic data are presented.

Keywords

Independent Component Analysis Hierarchical Agglomerative Cluster Latent Variable Model Agglomerative Cluster Differential Entropy 
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

  • Angelo Ciaramella
    • 1
  • Giuseppe Longo
    • 2
  • Antonino Staiano
    • 1
  • Roberto Tagliaferri
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of SalernoFisciano, Salerno
  2. 2.Dipartimento di Scienze FisicheUniversity of Naples, Polo delle Scienze e della TecnologiaNapoliItaly

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