Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data / Edition 1
by Hans-Hermann Bock
This first systematic and self-contained monograph on "Symbolic Data A nalysis" presents the most recent methods for analyzing and visualizin g symbolic data. It generalizes classical methods of exploratory, stat istical 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 numbe rs, intervals
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This first systematic and self-contained monograph on "Symbolic Data A nalysis" presents the most recent methods for analyzing and visualizin g symbolic data. It generalizes classical methods of exploratory, stat istical 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 numbe rs, intervals, or probability distributions. Typical methods include: graphical displays using Zoom Stars, visualization and future extracti on by symbolic factor analysis, decision trees, discrimination, classi fication, and clustering methods. Several benchmark examples from Nati onal Statistical Offices illustrate the usefulness of the methods. The book contains an extensive bibliography and a subject index.
Product Details
- ISBN-13:
- 9783540666196
- Publisher:
- Springer Berlin Heidelberg
- Publication date:
- 02/03/2000
- Series:
- Studies in Classification, Data Analysis, and Knowledge Organization Series
- Edition description:
- 2000
- Pages:
- 425
- Product dimensions:
- 6.69(w) x 9.53(h) x 0.04(d)
Table of Contents
E. Diday: Symbolic Data Analysis and the SODAS Project: Purpose, History, Perspective.- H.H. Bock: The Classical Data Situation.- H.H. Bock: Symbolic Data.- H.H. Bock, E. Diday: Symbolic Objects.- V. Stéphan, G. Hébrail, Y. Lechevallier: Generation of Symbolic Objects from Relational Databases.- P. Bertrand, F. Goupil: Descriptive Statistics for Symbolic Data.- M. Noirhomme-Fraiture, M. Rouard: Visualizing and Editing Symbolic Objects.- Similarity and Dissimilarity: F. Esposito, D. Malerba, V. Tamma, H.H. Bock: Classical Resemblance Measures.- H.H. Bock: Dissimilarity Measures for Probability Distributions.- F. Esposito, D. Malerba, V. Tamma: Dissimilarity Measures for Symbolic Objects.- F. Esposito, D. Malerba, F. Lisi: Matching Symbolic Objects.- Symbolic Factor Analysis: H.H.Bock: Classical Principal Component Analysis.- A. Chouakria, P. Cazes, E. Diday: Symbolic Principal Component Analysis.- N.C. Lauro, F. Palumbo, R. Verde: Factorial Discriminant Analysis on Symbolic Objects.- Discrimination: Assigning Symbolic Objects to Classes: J. Rasson, S. Lissoir: Classical Methods of Discrimination.- J. Rasson, S. Lissoir: Symbolic Kernel Discriminant Analysis.- E. Périnel, Y. Lechevalier: Symbolic Discrimination Rules.- M. Bravo Llatas, J. Garcia-Santesmases: Segmentation Trees for Stratified Data.- Clustering Methods for Symbolic Objects: M. Chavent, H.H. Bock: Clustering Problem, Clustering Methods for Classical Data.- M. Chavent: Criterion-Based Divisive Clustering for Symbolic Data.- P. Brito: Hierarchical and Pyramidal Clustering with Complete Symbolic Objects.- G. Polaillon: Pyramidal Classification for Interval Data Using Galois Lattice Reduction.- M. Gettler-Summa, C. Pardoux: Symbolic Approaches for Three-way Data.-Illustrative Benchmark Analysis: R. Bisdorff: Introduction.- R. Bisdorff: Professional Careers of Retired Working Persons.- A. Iztueta, P. Calvo: Labour Force Survey.- F. Goupil, M. Touati, E. Diday, R. Moult: Census Data from the Office for National Statistics.- A. Morineau: The SODAS Software Package.
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