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

Analysis of Symbolic Data Exploratory Methods for Extracting Statistical Information from Complex Data / edited by Hans-Hermann Bock, Edwin Diday.

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...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Bock, Hans-Hermann (Editor), Diday, Edwin (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2000.
Edition:1st ed. 2000.
Series:Studies in Classification, Data Analysis, and Knowledge Organization,
Springer eBook Collection.
Subjects:
Probabilities.
Statistics .
Economic theory.
Data structures (Computer science).
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Symbolic Data Analysis and the SODAS Project: Purpose, History, Perspective
  • 1.1 Introduction
  • 1.2 Symbolic Data Tables and Symbolic Objects
  • 1.3 Tools and Operations for Symbolic Objects
  • 1.4 History and Evolution of SDA
  • 1.5 The Content of the SODAS Project
  • 1.6 Philosophical Background: Concepts and Symbolic Objects
  • 1.7 Advantages of Using Symbolic Data Analysis
  • 1.8 The Future Development of SODAS
  • 2 The Classical Data Situation
  • 2.1 Introduction
  • 2.2 Variables as Input Data
  • 2.3 Quantitative Variables
  • 2.4 Qualitative Variables
  • 2.5 Data Vectors and the Data Matrix
  • 2.6 Dependent Variables
  • 2.7 Missing Values
  • 3 Symbolic Data
  • 3.1 Three Introductory Examples
  • 3.2 Multi-Valued and Interval Variables
  • 3.3 Modal Variables
  • 3.4 A Synthesis of Symbolic Data Types
  • 3.5 The Symbolic Data Array
  • 4 Symbolic Objects
  • 4.1 Introduction and Examples
  • 4.2 Relations and Descriptions
  • 4.3 Events and Assertion Objects
  • 4.4 Boolean Symbolic Objects as Triples
  • 4.5 Modal Symbolic Objects
  • 5 Generation of Symbolic Objects from Relational Databases
  • 5.1 Introduction to Relational Databases
  • 5.2 Principles of Symbolic Object Acquisition from Relational Databases
  • 5.3 Interaction with the Database
  • 5.4 A Generalization Operator
  • 5.5 Further Operations on Generated Assertions
  • 6 Descriptive Statistics for Symbolic Data
  • 6.1 Descriptive Statistics for a Classical Numerical Variable
  • 6.2 The Observed Symbolic Data Set
  • 6.3 The Case of Multi-Valued Variables
  • 6.4 The Case of an Interval-Valued Variable
  • 7 Visualizing and Editing Symbolic Objects
  • 7.1 The Zoom Star Representation
  • 7.2 Editing Symbolic Objects
  • 8 Similarity and Dissimilarity
  • 8.1 Classical Resemblance Measures
  • 8.2 Dissimilarity Measures for Probability Distributions
  • 8.3 Dissimilarity Measures for Symbolic Objects
  • 8.4 Matching Symbolic Objects
  • 9 Symbolic Factor Analysis
  • 9.1 Classical Principal Component Analysis
  • 9.2 Symbolic Principal Component Analysis
  • 9.3 Factorial Discriminant Analysis on Symbolic Objects
  • 10 Discrimination: Assigning Symbolic Objects to Classes
  • 10.1 Classical Methods of Discrimination
  • 10.2 Symbolic Kernel Discriminant Analysis
  • 10.3 Symbolic Discrimination Rules
  • 10.4 Segmentation Trees for Stratified Data
  • 11 Clustering Methods for Symbolic Objects
  • 11.1 Clustering Problem, Clustering Methods for Classical Data
  • 11.2 Criterion-Based Divisive Clustering for Symbolic Data
  • 11.3 Hierarchical and Pyramidal Clustering with Complete Symbolic Objects
  • 11.4 Pyramidal Classification for Interval Data Using Galois Lattice Reduction
  • 12 Symbolic Approaches for Three-way Data
  • 12.1 Introduction
  • 12.2 The Input and Output Data
  • 12.3 Processing Temporal Data
  • 12.4 Interpretation of Outcomes from Processing of Temporal Changes
  • 12.5 Real-Case Examples
  • 13 Illustrative Benchmark Analyses
  • 13.1. Introduction
  • 13.2 Professional Careers of Retired Working Persons
  • 13.3 Comparing European Labour Force Survey Results from the Basque Country and Portugal
  • 13.4 Processing Census Data from ONS
  • 13.5 General Conclusion
  • 14 The SODAS Software Package
  • 14.1 Short Introduction to the SODAS Software
  • 14.2 Short Processing of a Chaining
  • 14.3 Short List of Methods in SODAS Software
  • Notations and Abbreviations
  • Addresses of Contributors to this Volume.

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