Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data (Studies in Classification, Data Analysis, and Knowledge Organization)

ISBN 10: 3540666192 / ISBN 13: 9783540666196

Published by Springer, 1999

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Bibliographic Details

• Title: Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data (Studies in Classification, Data Analysis, and Knowledge Organization)
• Publisher: Springer
• Publication Date: 1999
• Language: English
• ISBN 10: 3540666192
• ISBN 13: 9783540666196
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Raymond Bisdorff CRP-GL, Luxembourg The development of the SODAS software based on symbolic data analysis was extensively described in the previous chapters of this book. It was accompanied by a series of benchmark activities involving some official statistical institutes throughout Europe. Partners in these benchmark activities were the National Statistical Institute (INE) of Portugal, the Instituto Vasco de Estadistica Euskal (EUSTAT) from Spain, the Office For National Statistics (ONS) from the United Kingdom, the Inspection Generale de la Securite Sociale (IGSS) from Luxembourg 1 and marginally the University of Athens . The principal goal of these benchmark activities was to demonstrate the usefulness of symbolic data analysis for practical statistical exploitation and analysis of official statistical data. This chapter aims to report briefly on these activities by presenting some signifi­ cant insights into practical results obtained by the benchmark partners in using the SODAS software package as described in chapter 14 below.

Synopsis

This first systematic and self-contained monograph on "Symbolic Data Analysis" presents the most recent methods for analyzing and visualizing symbolic data. It generalizes classical methods of exploratory, statistical and graphical data analysis to the case of complex data where the entries of a data table are, e.g., sets of categories or of numbers, intervals or probability distributions. Typical methods include: graphical displays using Zoom Stars, visualization and feature extraction by symbolic factor analysis, decision trees, discrimination, classification and clustering methods. Several benchmark examples from National Statistical Offices illustrate the usefulness of the methods. The book contains an extensive bibliography and a subject index.