An investigation of nine procedures for detecting the structure in a data set

  • André Hardy
  • Paul Andre
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


A problem common to all clustering techniques is the difficulty of deciding the number of clusters present in the data. The aim of this paper is to assess the performance of the best stopping rules from the Milligan and Cooper’s (1985) study, on specific artificial data sets containing a particular cluster structure. To provide a variety of solutions the data sets are analysed by four clustering procedures. We compare also these results with those obtained by three methods based on the hypervolume clustering criterion.


Clustering stopping rule number of clusters hypervolume criterion 


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Copyright information

© Springer-Verlag Berlin · Heidelberg 1998

Authors and Affiliations

  • André Hardy
    • 1
  • Paul Andre
    • 1
  1. 1.Unité de Statistique, Département de MathématiqueFacultés Universitaires N.-D. de la PaixNamurBelgium

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