Symbolic Data Analysis and the SODAS Software

Symbolic Data Analysis and the SODAS Software

Editor(s): Edwin Diday, Monique Noirhomme-Fraiture

Published Online: 28 JAN 2008

Print ISBN: 9780470018835

Online ISBN: 9780470723562

DOI: 10.1002/9780470723562

About this Book

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.

Table of contents

    1. You have free access to this content
  1. Part I: Databases versus Symbolic Objects

    1. Chapter 3

      Exporting Symbolic Objects to Databases (pages 61–66)

      Donato Malerba, Floriana Esposito and Annalisa Appice

    2. Chapter 5

      Editing Symbolic Data (pages 81–92)

      Monique Noirhomme-Fraiture, Paula Brito, Anne de Baenst-Vandenbroucke and Adolphe Nahimana

    3. Chapter 6

      The Normal Symbolic Form (pages 93–107)

      Marc Csernel and Francisco de A. T. de Carvalho

    4. Chapter 7

      Visualization (pages 109–120)

      Monique Noirhomme-Fraiture and Adolphe Nahimana

  2. Part II: Unsupervised Methods

    1. Chapter 8

      Dissimilarity and Matching (pages 121–148)

      Floriana Esposito, Donato Malerba and Annalisa Appice

    2. Chapter 9

      Unsupervised Divisive Classification (pages 149–156)

      Jean-Paul Rasson, Jean-Yves Pirçon, Pascale Lallemand and Séverine Adans

    3. Chapter 11

      Clustering Methods in Symbolic Data Analysis (pages 181–203)

      Francisco de A. T. de Carvalho, Yves Lechevallier and Rosanna Verde

    4. Chapter 16

      Generalized Canonical Analysis (pages 313–330)

      N. Carlo Lauro, Rosanna Verde and Antonio Irpino

  3. Part III: Supervised Methods

    1. Chapter 17

      Bayesian Decision Trees (pages 331–340)

      Jean-Paul Rasson, Pascale Lallemand and Séverine Adans

    2. Chapter 18

      Factor Discriminant Analysis (pages 341–358)

      N. Carlo Lauro, Rosanna Verde and Antonio Irpino

    3. Chapter 19

      Symbolic Linear Regression Methodology (pages 359–372)

      Filipe Afonso, Lynne Billard, Edwin Diday and Mehdi Limam

  4. Part IV: Applications and the Sodas Software

    1. You have free access to this content

SEARCH