Some Trends in the Classification of Variables

  • F. Costa Nicolau
  • H. Bacelar-Nicolau
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


In this paper we review a class of hierarchical clustering methods based on similarity coefficients and aggregation criteria which are associated to the integral transformation by the (probabilistic) distribution function of some suitable sample statistics. Some properties of those methods we have studied are remembered and/or derived here. Applications on either simulated or real data set have shown this approach performs better than the traditional one (using empirical clustering methods) in many situations. Moreover we define some “hybrid” criteria, which we generalise in order to get some mixed or parametric hierarchical clustering methods. Inside of such parametrical families we are able to find, among different criteria those better fitting to the initial similarities, and to search for stability and validity of those methods.


Similarity Coefficient Parametric Family Single Linkage Level Index Aggregation Criterion 


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Copyright information

© Springer Japan 1998

Authors and Affiliations

  • F. Costa Nicolau
    • 1
  • H. Bacelar-Nicolau
    • 2
  1. 1.Department of Mathematics, Faculty of Sciences and Technology, Laboratory of Statistics and Actuarial Mathematics (LEMA)New University of LisbonPortugal
  2. 2.Laboratory of Statistics and Data Analysis (LEAD), CEA / JNICTUniversity of Lisbon, Faculty of Psychology and EducationPortugal

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