Hierarchical Conceptual Clustering

  • J. F. Puget
  • N. Benamou
  • C. Vrain
  • Y. Kodratoff
Conference paper


In this paper the following problem is studied : how a hierarchical conceptual classification of a given set of examples can be discovered ?

Traditional techniques for this purpose, developed in numerical data analysis, are often inadequate because they cluster objects solely on the basis of a numerical measure of similarity. Thus the clusters obtained have no simple descriptions. This limitation is overcome by the conceptual hierarchical methods shown here .

Firstly, the algorithm CLUSTER/2 is introduced. We describe the representation language used, the basic routines, and show how it works on an example.

Secondly, we are developing a conceptual hierarchy building method based on the similarity between events or event sets using the notion of Similarity vectors. The algorithm outputs several equally sensible hierarchies.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1986

Authors and Affiliations

  • J. F. Puget
    • 1
  • N. Benamou
    • 1
  • C. Vrain
    • 1
  • Y. Kodratoff
    • 1
  1. 1.Laboratoire de Recherche en InformatiqueUA 410 du CNRS, Universite de Paris-SudBatimentOrsayFrance

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