An examination of procedures for determining the number of clusters in a data set

  • André Hardy
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 compare three methods based on the hypervolume criterion with four other well-known methods. This evaluation of procedures for determining the number of clusters is conducted on artificial data sets. To provide a variety of solutions the data sets are analysed by six clustering methods. We finally conclude by pointing out the performance of each method and by giving some guidance for making choices between them.


Convex Hull Cluster Method Poisson Process Cluster Procedure Optimal Partition 
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 1994

Authors and Affiliations

  • André Hardy
    • 1
    • 2
  1. 1.Unité de Statistique, Département de MathématiqueFacultés Universitaires N.-D. de la PaixNamurBelgium
  2. 2.Facultés Universitaires Saint-LouisBruxellesBelgium

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