Ebook: Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data

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.

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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.

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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.
Content:
Front Matter....Pages i-xviii
Symbolic Data Analysis and the SODAS Project: Purpose, History, Perspective....Pages 1-23
The Classical Data Situation....Pages 24-38
Symbolic Data....Pages 39-53
Symbolic Objects....Pages 54-77
Generation of Symbolic Objects from Relational Databases....Pages 78-105
Descriptive Statistics for Symbolic Data....Pages 106-124
Visualizing and Editing Symbolic Objects....Pages 125-138
Similarity and Dissimilarity....Pages 139-197
Symbolic Factor Analysis....Pages 198-233
Discrimination: Assigning Symbolic Objects to Classes....Pages 234-293
Clustering Methods for Symbolic Objects....Pages 294-341
Symbolic Approaches for Three-way Data....Pages 342-354
Illustrative Benchmark Analyses....Pages 355-385
The SODAS Software Package....Pages 386-391
Back Matter....Pages 392-425

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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.
Content:
Front Matter....Pages i-xviii
Symbolic Data Analysis and the SODAS Project: Purpose, History, Perspective....Pages 1-23
The Classical Data Situation....Pages 24-38
Symbolic Data....Pages 39-53
Symbolic Objects....Pages 54-77
Generation of Symbolic Objects from Relational Databases....Pages 78-105
Descriptive Statistics for Symbolic Data....Pages 106-124
Visualizing and Editing Symbolic Objects....Pages 125-138
Similarity and Dissimilarity....Pages 139-197
Symbolic Factor Analysis....Pages 198-233
Discrimination: Assigning Symbolic Objects to Classes....Pages 234-293
Clustering Methods for Symbolic Objects....Pages 294-341
Symbolic Approaches for Three-way Data....Pages 342-354
Illustrative Benchmark Analyses....Pages 355-385
The SODAS Software Package....Pages 386-391
Back Matter....Pages 392-425
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