Chapter 11. Clustering Methods in Symbolic Data Analysis

  1. Edwin Diday4 and
  2. Monique Noirhomme-Fraiture5
  1. Francisco de A. T. de Carvalho1,
  2. Yves Lechevallier2 and
  3. Rosanna Verde3

Published Online: 28 JAN 2008

DOI: 10.1002/9780470723562.ch11

Symbolic Data Analysis and the SODAS Software

Symbolic Data Analysis and the SODAS Software

How to Cite

Carvalho, F. d. A. T. d., Lechevallier, Y. and Verde, R. (2008) Clustering Methods in Symbolic Data Analysis, 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.ch11

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

    Universade Federale de Pernambuco, Centro de Informatica, Av. Prof. Luis Freire s/n - Citade Universitaria, Recife-PE, Brazil, 50740-540

  2. 2

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

  3. 3

    Dipartimento di Studi Europei e Mediterranei, Seconda Universitádegli Studi di Napoli, Via del Setificio, 15 Complesso Monumentale Belvedere di S. Leucio, 81100 Caserta, Italy

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:

  • classical dynamic clustering method;
  • symbolic clustering algorithm;
  • representation step and allocation step;
  • total sum of squares (TSS), within sum of squares (WSS) and between sum of squares (BSS);
  • nearest-neighbour algorithm criterion;
  • interval-valued data clusters;
  • allocation function and dissimilarity function;
  • description potential function;
  • contextdependent proximity function;
  • categorical multi-valued or interval descriptors

Summary

This chapter contains sections titled:

  • Introduction

  • Symbolic dynamic clustering approaches

  • Symbolic dynamic clustering algorithm (SCLUST)

  • Clustering algorithm on distance tables (DCLUST)

  • Cluster interpretative aids

  • Applications

  • References