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Full Description
The Structural Representation of Proximity Matrices with MATLAB presents and demonstrates the use of functions (by way of M-files) within a MATLAB computational environment to effect a variety of structural representations for the proximity information that is assumed to be available on a set of objects. The representations included in the book have been developed primarily in the behavioral sciences and applied statistical literature (e.g., in psychometrics and classification), although interest in these topics now extends more widely to such fields as bioinformatics and chemometrics.
Throughout the book, two kinds of proximity information are analyzed: one-mode and two-mode. One-mode proximity data are defined between the objects from a single set and are usually given in the form of a square symmetric matrix; two-mode proximity data are defined between the objects from two distinct sets and are given in the form of a rectangular matrix. In addition, there is typically the flexibility to allow the additive fitting of multiple structures to either the given one- or two-mode proximity information.
This book is divided into three main sections, each based on the general class of representations being discussed. Part I develops linear and circular unidimensional and multidimensional scaling using the city-block metric as the major representational device. Part II discusses characterizations based on various graph-theoretic tree structures, specifically those referred to as ultrametrics and additive trees. Part III uses representations defined solely by order properties, particularly emphasizing what are called (strongly) anti-Robinson forms.
Contents
List of Figures
List of Tables
Preface
Part I: (Multi- and Unidimensional) City-Block Scaling. Chapter 1: Linear Unidimensional Scaling
Chapter 2: Linear Multidimensional Scaling
Chapter 3: Circular Scaling
Chapter 4: LUS for Two-Mode Proximity Data
Part II: The Representation of Proximity Matrices by Tree Structures. Chapter 5: Ultrametrics for Symmetric Proximity Data
Chapter 6: Additive Trees for Symmetric Proximity Data
Chapter 7: Fitting Multiple Tree Structures to a Symmetric Proximity Matrix
Chapter 8: Ultrametrics and Additive Trees for Two-Mode (Rectangular) Proximity Data
Part III: The Representation of Proximity Matrices by Structures Dependent on Order (Only). Chapter 9: Anti-Robinson Matrices for Symmetric Proximity Data
Chapter 10: Circular Anti-Robinson Matrices for Symmetric Proximity Data
Chapter 11: Anti-Robinson Matrices for Two-Mode Proximity Data
Appendix A: Header Comments for the M-Files Mentioned in the Text and Given in Alphabetical Order
Bibliography
Indices.
