This volume describes new methods with special emphasis on classification and cluster analysis. These methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.

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.

This volume is a logical extension of #11, Multidimensional Scaling, providing an up-to-date overview of some three-way modesl for multidimensional scaling and related techniques.104

Combinatorial data analysis (CDA) refers to a wide class of methods for the study of relevant data sets in which the arrangement of a collection of objects is absolutely central. The focus of this monograph is on the identification of arrangements, which are then further restricted to where the combinatorial search is carried out by a recursive optimization process based on the general principles of dynamic programming (DP).

The authors provide a comprehensive and self-contained review delineating a very general DP paradigm or schema that can serve two functions. First, the paradigm can be applied in various special forms to encompass all previously proposed applications suggested in the classification literature. Second, the paradigm can lead directly to many more novel uses. An appendix is included as a user's manual for a collection of programs available as freeware.

The incorporation of a wide variety of CDA tasks under one common optimization framework based on DP is one of the book's strongest points. The authors include verifiably optimal solutions to nontrivially sized problems over the array of data analysis tasks discussed.