Symbolic Data Analysis and the SODAS Software (eBook, PDF)

Redaktion: Diday, Edwin; Noirhomme-Fraiture, Monique

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 476
• Erscheinungstermin: 2. August 2008
• Englisch
• ISBN-13: 9780470723555
• Artikelnr.: 37298706

Autorenporträt

Edwin Diday, Centre De Recherche en Mathématiques de la Décision, 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 led international research teams in Symbolic Data Analysis, and is the founder of the field. M. Noirhomme-Fraiture, Institute of Computer Science, University of Namur, Belgium Monique Noirhomme-Fraiture is Professor and Head of the Unit of Applied Mathematics at the above faculty. She is involved in several HCI projects as well as having organized conferences and workshops within this field. She has contributed to 28 published papers and co-authored 2 books.

Inhaltsangabe

Contributors. Foreword. Preface. ASSO Partners. Introduction. 1. The state
of the art in symbolic data analysis: overview and future (Edwin Diday).
PART I. DATABASES VERSUS SYMBOLIC OBJECTS. 2. Improved generation of
symbolic objects from relational databases (Yves Lechevallier, Aicha El
Golli and George Hébrail). 3. Exporting symbolic objects to databases
(Donato Malerba, Floriana Esposito and Annalisa Appice). 4. A statistical
metadata model for symbolic objects (Haralambos Papageorgiou and Maria
Vardaki). 5. Editing symbolic data (Monique-Noirhomme-Fraiture, Paula
Brito, 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 Adolphe Nahimana). PART II.
UNSUPERVISED METHODS. 8. Dissimilarity and matching (Floriana Esposito,
Donato Malerba and Annalisa Appice). 9. Unsupervised divisive
classification (Jean-Paul Rasson, Jean-Yves Pirçon, Pascale Lallemand and
Séverine Adans). 10. Hierarchical and pyramidal clustering (Paula Brito and
Francisco de A.T. de Carvalho). 11 .Clustering methods in symbolic data
analysis (Francisco de A.T. de Carvalho, Yves Lechevallier and Rosanna
Verde). 12. Visualizing symbolic data by Kohonen maps (Hans-Hermann Bock).
13 .Validation of clustering structure: determination of the number of
clusters (André Hardy). 14. Stability measures for assessing a partition
and its clusters: application to symbolic data sets (Patrice Bertrand and
Ghazi Bel Mufti). 15. Principal component analysis of symbolic data
described by intervals (N.Carlo Lauro, Rosanna Verde and Antonio Irpino).
16. Generalized canonical analysis (N.Carlo Lauro, Rosanna Verde and
Antonio Irpino). PART III .SUPERVISED METHODS. 17. Bayesian decision trees
(Jean-Paul Rasson, Pascale Lallemand and Séverine Adans). 18. Factor
discriminant analysis (N.Carlo Lauro, Rosanna Verde and Antonio Irpino).
19. Symbolic linear regression methodology (Filipe Afonso, Lynne Billard,
Edwin Diday and Mehdi Limam). 20. Multi-layer perceptrons and symbolic data
(Fabrice Rossi and Brieuc Conan-Guez). PART IV. APPLICATION AND THE SODAS
SOFTWARE. 21. Application to the Finnish, Spanish and Portuguese data of
the European Social Survey (Soile Mustjärvi and Seppo Laaksonen). 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 Basque country (Marta Mas and Haritz Olaeta). 24. SODAS2
software: overview and methodology (Anne de Baenst-Vandenbroucke and Yves
Lechevallier). Index.