Symbolic Data Analysis And the Sodas Software

by Diday, Edwin; Noirhomme-Fraiture, Monique

• ISBN13: 9780470018835
• ISBN10: 0470018836
• Edition: 1st
• Format: Hardcover
• Copyright: 2008-03-10
• Publisher: Wiley-Interscience

Summary

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.

Author Biography

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.

Table of Contents

Contributors	p. ix
Foreword	p. xiii
Preface	p. xv
ASSO Partners	p. xvii
Introduction	p. 1
The state of the art in symbolic data analysis: overview and future	p. 3
Databases versus Symbolic Objects	p. 43
Improved generation of symbolic objects from relational databases	p. 45
Exporting symbolic objects to databases	p. 61
A statistical metadata model for symbolic objects	p. 67
Editing symbolic data	p. 81
The normal symbolic form	p. 93
Visualization	p. 109
Unsupervised Methods	p. 121
Dissimilarity and matching	p. 123
Unsupervised divisive classification	p. 149
Hierarchical and pyramidal clustering	p. 157
Clustering methods in symbolic data analysis	p. 181
Visualizing symbolic data by Kohonen maps	p. 205
Validation of clustering structure: determination of the number of clusters	p. 235
Stability measures for assessing a partition and its clusters: application to symbolic data sets	p. 263
Principal component analysis of symbolic data described by intervals	p. 279
Generalized canonical analysis	p. 313
Supervised Methods	p. 331
Bayesian decision trees	p. 333
Factor discriminant analysis	p. 341
Symbolic linear regression methodology	p. 359
Multi-layer perceptrons and symbolic data	p. 373
Applications and the SODAS Software	p. 393
Application to the Finnish, Spanish and Portuguese data of the European Social Survey	p. 395
People's life values and trust components in Europe: symbolic data analysis for 20-22 countries	p. 405
Symbolic analysis of the Time Use Survey in the Basque country	p. 421
SODAS2 software: Overview and methodology	p. 429
Index	p. 445
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