Chapter 12. Visualizing Symbolic Data by Kohonen Maps

  1. Edwin Diday2 and
  2. Monique Noirhomme-Fraiture3
  1. Hans-Hermann Bock

Published Online: 28 JAN 2008

DOI: 10.1002/9780470723562.ch12

Symbolic Data Analysis and the SODAS Software

Symbolic Data Analysis and the SODAS Software

How to Cite

Bock, H.-H. (2007) Visualizing Symbolic Data by Kohonen Maps, in Symbolic Data Analysis and the SODAS Software (eds E. Diday and M. Noirhomme-Fraiture), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470723562.ch12

Editor Information

  1. 2

    Université Paris IX-Dauphine, LISE-CEREMADE, Place du Marechal de Lattre de Tassigny, Paris Cedex 16, France F-75775

  2. 3

    Facultés Universitaires Notre-Dame de la Paix, Faculté d'Informatique, Rue Grandgagnage, 21, Namur, Belgium, B-5000

Author Information

  1. Rheinisch-Westfälische Technische Hochschule Aachen, Institut für Statistik und Wirtschaftmathematik, Wüllnerstr. 3, Aachen, Germany, D-52056

Publication History

  1. Published Online: 28 JAN 2008
  2. Published Print: 18 JAN 2007

ISBN Information

Print ISBN: 9780470018835

Online ISBN: 9780470723562

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Keywords:

  • symbolic principal component analysis (SPCA);
  • symbolic generalized canonical analysis (SGCA);
  • clustering and class prototype construction;
  • Kohonen and SYKSOM approach;
  • dissimilarity measures, cluster prototypes and kernel functions;
  • principal component analysis (PCA);
  • rectangular lattice;
  • topological correctness;
  • interval-type data matrix;
  • prototype hypercube

Summary

This chapter contains sections titled:

  • Introduction

  • Kohonen maps

  • Technical definitions and methodological options

  • The StochApprox and MacQueen algorithms

  • References