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Dynamical Clustering of Interval Data: Optimization of an Adequacy Criterion Based on Hausdorff Distance

  • Marie Chavent
  • Yves Lechevallier
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

In order to extend the dynamical clustering algorithm to interval data sets, we define the prototype of a cluster by optimization of a classical adequacy criterion based on Hausdorff distance. Once this class prototype properly defined we give a simple and converging algorithm for this new type of interval data.

Keywords

Hausdorff Distance Interval Data Symbolic Data Dynamical Cluster Adequacy Criterion 
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 2002

Authors and Affiliations

  • Marie Chavent
    • 1
  • Yves Lechevallier
    • 2
  1. 1.Mathématiques Appliquées de Bordeaux, UMR 5466 CNRSUniversité Bordeaux 1-351Talence CedexFrance
  2. 2.Domaine de Voluceau- RocquencourtINRIA- Institut National de Recherche en Informatique et en AutomatiqueLe Chesnay CedexFrance

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