Computer Graphics for Multivariate Data

  • R. Cléroux
  • Y. Lepage
  • N. Ranger


The exploration of multidimensional data involves the use of a set of empirical techniques which aid in the discovery of interesting avenues to be pursued in later statistical analysis. Data exploration often directs this analysis. The availability and power of computers has changed the nature of statistical work and has made the exploration of multidimensional data more accessible. Graphical methods constitute one of the main tools for data exploration, and they are therefore of primary importance.

In this article, four graphical representation methods are presented and applied to atmospheric pollution data for the Montreal region. These representations enable the data from each monitoring station to be visualized and grouping may then be formed from observed similarities.


Daily Maximum Multivariate Data Multidimensional Data Division Point Empirical Technique 
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 Tokyo 1985

Authors and Affiliations

  • R. Cléroux
    • 1
  • Y. Lepage
    • 2
  • N. Ranger
    • 1
  1. 1.Département d’informatique et de recherche opérationnelleUniversité de MontréalMontréalCanada
  2. 2.Département de mathématiques et de statistiqueUniversité de MontréalMontréalCanada

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