Symbolic Data Analysis and the SODAS Software (Google eBook)

Edwin Diday, Monique Noirhomme-Fraiture

John Wiley & Sons, Apr 15, 2008 - Mathematics - 476 pages

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.

Contents

• ASSO Partners: xvii
• Introduction: 1
• 1 The state of the art in symbolic data analysis overview and future: 3
• Part I Databases versus Symbolic Objects: 43
• 2 Improved generation of symbolic objects from relational databases: 45
• 3 Exporting symbolic objects to databases: 61
• 4 A statistical metadata model for symbolic objects: 67
• 5 Editing symbolic data: 81
• 6 The normal symbolic form: 93
• 7 Visualization: 109
• Part II Unsupervised Methods: 121
• 8 Dissimilarity and matching: 123
• 9 Unsupervised divisive classification: 149
• 10 Hierarchical and pyramidal clustering: 157
• 11 Clustering methods in symbolic data analysis: 181
• 12 Visualizing symbolic data by Kohonen maps: 205
• 13 Validation of clustering structure determination of the number of clusters: 235
• 14 Stability measures for assessing a partition and its clusters application to symbolic data sets: 263
• 15 Principal component analysis of symbolic data described by intervals: 279
• 16 Generalized canonical analysis: 313
• Part III Supervised Methods: 331
• 17 Bayesian decision trees: 333
• 18 Factor discriminant analysis: 341
• 19 Symbolic linear regression methodology: 359
• 20 Multilayer perceptrons and symbolic data: 373
• Part IV Applications and the SODAS Software: 393
• 21 Application to the Finnish Spanish and Portuguese data of the European Social Survey: 395
• 22 Peoples life values and trust components in Europe symbolic data analysis for 2022 countries: 405
• 23 Symbolic analysis of the Time Use Survey in the Basque country: 421
• 24 SODAS2 software Overview and methodology: 429
• Index: 445

About the author

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.

Bibliographic information

• Title: Symbolic Data Analysis and the SODAS Software
• Editors: Edwin Diday, Monique Noirhomme-Fraiture
• Publisher: John Wiley & Sons, 2008
• ISBN: 0470723556, 9780470723555
• Length: 476 pages
• Subjects: Mathematics / Probability & Statistics / General