Chapter 2. Improved Generation of Symbolic Objects from Relational Databases

  1. Edwin Diday4,
  2. Monique Noirhomme-Fraiture5
  1. Yves Lechevallier1,
  2. Aicha El Golli2,
  3. George Hébrail3

Published Online: 28 JAN 2008

DOI: 10.1002/9780470723562.ch2

Book Title

Symbolic Data Analysis and the SODAS Software

Symbolic Data Analysis and the SODAS Software

Additional Information

How to Cite

Lechevallier, Y., Golli, A. E. and Hébrail, G. (2008) Improved Generation of Symbolic Objects from Relational Databases, 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.ch2

Editor Information

  1. 4

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

  2. 5

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

Author Information

  1. 1

    INRIA, Unité de Recherche de Roquencourt, Domaine de Voluceau, BP 105, Le Chesnay Cedex, France, F-78153

  2. 2

    INRIA Paris, Unité de Recherche de Roquencourt, Domaine de Voluceau, BP 105, Le Chesnay Cedex, France, F-78153

  3. 3

    Laboratoire LTCI UMR 5141, Ecole Nationale Supériewe des Télécommunications, 46 rue Barrault, 75013 Paris, France

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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CHAPTER TOOLS

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Keywords:

  • observed values interval;
  • Symbolic object refinement;
  • boolean multi-valued variables;
  • Criterion-based divisive clustering algorithm;
  • within-cluster criterion;
  • quantitative/ordinal qualitative case;
  • assertions disjunctions;
  • standard clustering techniques;
  • K-nearest-neighbour techniques;
  • hoard structure

Summary

This chapter contains sections titled:

  • Introduction

  • Construction of symbolic objects by generalization

  • Criterion-based divisive clustering algorithm

  • Improving the generalization process by decomposition

  • Applications

  • Conclusion

  • References

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