Symbolic Data Analysis and the SODAS Software / Edition 1

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Classical statistical techniques are often inadequate when it comes to analysing some of the large and internally variable datasets common today. Symbolic Data Analysis (SDA) has evolved in response to this problem and is a vital tool for summarizing information in such a way that the resulting data is of a manageable size. Symbolic data, represented by intervals, lists, histograms, distributions, curves and the like, keeps the "internal variation" of summaries better than standard data. SDA therefore plays a key role in the interaction between statistics and data processing, and has established itself as an important tool for analysing official statistics.

Through an extension of the concepts employed in data mining, the Editors provide an advanced guide to the techniques required to analyse symbolic data. Contributions from leading experts in the field enable the reader to build models and make predictions about future events.

The book: Provides new graphical tools for the interpretation of large data sets, Extends standard statistics, data analysis, data mining and knowledge discovery to symbolic data, Introduces the SODAS software, which is complementary to existing data analysis software (e.g. SAS, SPSS, SPAD) that are unable to work on symbolic data, Induces, exports, and compares knowledge from one database to another, Features a supporting website hosting the software, and user manual.

Symbolic Data Analysis and the SODAS Software is primarily aimed at practitioners of symbolic data analysis, such as statisticians and economists, within both the public and private sectors. There is also much of interest to postgraduate students and researchers within web mining, textmining, and bioengineering.

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Product Details

  • ISBN-13: 9780470018835
  • Publisher: Wiley, John & Sons, Incorporated
  • 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 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.

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Table of Contents

Contributors     ix
Foreword     xiii
Preface     xv
ASSO Partners     xvii
Introduction     1
The state of the art in symbolic data analysis: overview and future   Edwin Diday     3
Databases versus Symbolic Objects     43
Improved generation of symbolic objects from relational databases   Yves Lechevallier   Aicha El Golli   George Hebrail     45
Exporting symbolic objects to databases   Donato Malerba   Floriana Esposito   Annalisa Appice     61
A statistical metadata model for symbolic objects   Haralambos Papageorgiou   Maria Vardaki     67
Editing symbolic data   Monique Noirhomme-Fraiture   Paula Brito   Anne de Baenst-Vandenbroucke   Adolphe Nahimana     81
The normal symbolic form   Marc Csernel   Francisco de A.T. de Carvalho     93
Visualization   Monique Noirhomme-Fraiture   Adolphe Nahimana     109
Unsupervised Methods     121
Dissimilarity and matching   Floriana Esposito   Donato Malerba   Annalisa Appice     123
Unsupervised divisive classification   Jean-Paul Rasson   Jean-Yves Pircon   Pascale Lallemand   Severine Adans     149
Hierarchical and pyramidal clustering   Paula Brito   Francisco de A.T. de Carvalho     157
Clustering methods in symbolic data analysis   Francisco de A.T. de Carvalho   Yves Lechevallier   Rosanna Verde     181
Visualizing symbolic data by Kohonen maps   Hans-Hermann Bock     205
Validation of clustering structure: determination of the number of clusters   Andre Hardy     235
Stability measures for assessing a partition and its clusters: application to symbolic data sets   Patrice Bertrand   Ghazi Bel Mufti     263
Principal component analysis of symbolic data described by intervals   N. Carlo Lauro   Rosanna Verde   Antonio Irpino     279
Generalized canonical analysis   N. Carlo Lauro   Rosanna Verde   Antonio Irpino     313
Supervised Methods     331
Bayesian decision trees   Jean-Paul Rasson   Pascale Lallemand   Severine Adans     333
Factor discriminant analysis   N. Carlo Lauro   Rosanna Verde   Antonio Irpino     341
Symbolic linear regression methodology   Filipe Afonso   Lynne Billard   Edwin Diday   Mehdi Limam     359
Multi-layer perceptrons and symbolic data   Fabrice Rossi   Brieuc Conan-Guez     373
Applications and the SODAS Software     393
Application to the Finnish, Spanish and Portuguese data of the European Social Survey   Soile Mustjarvi   Seppo Laaksonen     395
People's life values and trust components in Europe: symbolic data analysis for 20-22 countries   Seppo Laaksonen     405
Symbolic analysis of the Time Use Survey in the Basque country   Marta Mas   Haritz Olaeta     421
SODAS2 software: Overview and methodology   Anne de Baenst-Vandenbroucke   Yves Lechevallier     429
Index     445
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