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Grade Models and Methods for Data Analysis: With Applications for the Analysis of Data Populations

✍ Scribed by Frederick Ruland (auth.), Prof. Teresa Kowalczyk, Prof. Elżbieta Pleszczyńska, Dr. Frederick Ruland (eds.)

Publisher
Springer-Verlag Berlin Heidelberg
Year
2004
Tongue
English
Leaves
482
Series
Studies in Fuzziness and Soft Computing 151
Edition
1
Category
Library
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✦ Synopsis

This book provides a new grade methodology for intelligent data analysis. It introduces a specific infrastructure of concepts needed to describe data analysis models and methods. This monograph is the only book presently available covering both the theory and application of grade data analysis and therefore aiming both at researchers, students, as well as applied practitioners. The text is richly illustrated through examples and case studies and includes a short introduction to software implementing grade methods, which can be downloaded from the editors.

✦ Table of Contents

Front Matter....Pages N1-x
Grade Data Analysis — A First Look....Pages 1-11
The Grade Approach....Pages 13-50
Univariate Lilliputian Model I....Pages 51-89
Univariate Lilliputian Model II....Pages 91-138
Asymmetry and the inverse concentration set....Pages 139-166
Discretization and regularity....Pages 167-184
Preliminary concepts of bivariate dependence....Pages 185-215
Dependence Lilliputian Model....Pages 217-265
Grade Correspondence Analysis and outlier detection....Pages 267-295
Cluster analysis based on GCA....Pages 297-324
Regularity and the number of clusters....Pages 325-338
Grade approach to the analysis of finite data matrices....Pages 339-384
Inequality measures for multivariate distributions....Pages 385-424
Case studies with multivariate data....Pages 425-453
The GradeStat program....Pages 455-457
Back Matter....Pages 459-477

✦ Subjects

Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics); Statistical Theory and Methods

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