Symbolic Data Analysis and the SODAS Software / Edition 1

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Overview

Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of very large datasets, and symbolic data analysis techniques have been developed in order to extract knowledge from such data. Symbolic data methods differ from that of data mining, for example, because rather than identifying points of interest in the data, symbolic data methods allow the user to build models of the data and make predictions about future events.
This book is the result of the work  f a pan-European project team led by Edwin Diday following 3 years work sponsored by EUROSTAT.  It includes a full explanation of the new SODAS software developed as a result of this project. The software and methods described highlight the crossover between statistics and computer science, with a particular emphasis on data mining.

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Product Details

  • ISBN-13: 9780470018835
  • Publisher: Wiley
  • Publication date: 3/25/2008
  • Edition number: 1
  • Pages: 476
  • Product dimensions: 6.81 (w) x 9.88 (h) x 1.04 (d)

Meet the Author

Edwin Diday, Centre De Recherche en Mathématiques de laDécision, Université Paris 9, France
Edwin is a Professor of Computer Science, with 50 published papers,and 14 authored or edited books to his name. He has ledinternational research teams in Symbolic Data Analysis, and is thefounder of the field.

M. Noirhomme-Fraiture, Institute of Computer Science,University of Namur, Belgium
Monique Noirhomme-Fraiture is Professor and Head of the Unit ofApplied Mathematics at the above faculty. She is involved inseveral HCI projects as well as having organized conferences andworkshops within this field. She has contributed to 28 publishedpapers and co-authored 2 books.

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Table of Contents

Contributors.

Foreword.

Preface.

ASSO Partners.

Introduction.

1. The state of the art in symbolic data analysis: overview andfuture (Edwin Diday).

PART I. DATABASES VERSUS SYMBOLIC OBJECTS.

2. Improved generation of symbolic objects from relationaldatabases (Yves Lechevallier, Aicha El Golli and GeorgeHébrail).

3. Exporting symbolic objects to databases (Donato Malerba,Floriana Esposito and Annalisa Appice).

4. A statistical metadata model for symbolic objects (HaralambosPapageorgiou and Maria Vardaki).

5. Editing symbolic data (Monique-Noirhomme-Fraiture, PaulaBrito, Anne de Baenst-Vandenbroucke and Adolphe Nahimana).

6. The normal symbolic form (Marc Csernel and Francisco de A.T.de Carvalho).

7. Visualization (Monique-Noirhomme-Fraiture and AdolpheNahimana).

PART II. UNSUPERVISED METHODS.

8. Dissimilarity and matching (Floriana Esposito, Donato Malerbaand Annalisa Appice).

9. Unsupervised divisive classification (Jean-Paul Rasson,Jean-Yves Pirçon, Pascale Lallemand and SéverineAdans).

10. Hierarchical and pyramidal clustering (Paula Brito andFrancisco de A.T. de Carvalho).

11 .Clustering methods in symbolic data analysis (Francisco deA.T. de Carvalho, Yves Lechevallier and Rosanna Verde).

12. Visualizing symbolic data by Kohonen maps (Hans-HermannBock).

13 .Validation of clustering structure: determination of thenumber of clusters (André Hardy).

14. Stability measures for assessing a partition and itsclusters: application to symbolic data sets (Patrice Bertrand andGhazi Bel Mufti).

15. Principal component analysis of symbolic data described byintervals (N.Carlo Lauro, Rosanna Verde and Antonio Irpino).

16. Generalized canonical analysis (N.Carlo Lauro, Rosanna Verdeand Antonio Irpino).

PART III .SUPERVISED METHODS.

17. Bayesian decision trees (Jean-Paul Rasson, Pascale Lallemandand Séverine Adans).

18. Factor discriminant analysis (N.Carlo Lauro, Rosanna Verdeand Antonio Irpino).

19. Symbolic linear regression methodology (Filipe Afonso, LynneBillard, Edwin Diday and Mehdi Limam).

20. Multi-layer perceptrons and symbolic data (Fabrice Rossi andBrieuc Conan-Guez).

PART IV. APPLICATION AND THE SODAS SOFTWARE.

21. Application to the Finnish, Spanish and Portuguese data ofthe European Social Survey (Soile Mustjärvi and SeppoLaaksonen).

22. People’s life values and trust components in Europe:symbolic data analysis for 20-22 countries (Seppo Laaksonen).

23. Symbolic analysis of the Time Use Survey in the Basquecountry (Marta Mas and Haritz Olaeta).

24. SODAS2 software: overview and methodology (Anne deBaenst-Vandenbroucke and Yves Lechevallier).

Index.

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