Branch-and-Bound Applications in Combinatorial Data Analysis

Cluster Analysis-Partitioning.- An Introduction to Branch-and-Bound Methods for Partitioning.- Minimum-Diameter Partitioning.- Minimum Within-Cluster Sums of Dissimilarities Partitioning.- Minimum Within-Cluster Sums of Squares Partitioning.- Multiobjective Partitioning.- Seriation.- to the Branch-and-Bound Paradigm for Seriation.- Seriation-Maximization of a Dominance Index.- Seriation-Maximization of Gradient Indices.- Seriation-Unidimensional Scaling.- Seriation-Multiobjective Seriation.- Variable Selection.- to Branch-and-Bound Methods for Variable Selection.- Variable Selection for Cluster Analysis.- Variable Selection for Regression Analysis.

From the reviews:

"To say it unadorned at the outset, this excellent mongraph by Brusco and Stahl on applications of branch-and-bound algorithms in combinatorial data anlaysis fills a long neglected gap in the quantitative psychology literature, and will prove an indispensable companion to any researcher having serious interest in advanced techniques for seriation/scaling and nonhierarchical clustering." Psychometrika

"This monograph is true to its purpose: developing and carefully explaining the application of branch-and-bound (B&B) algorithms for the solution of specific combinatorial optimization problems in statistics. ... One of the bright features of this book is the even handed way in which the B&B methods are presented. In each case, the authors clearly spell out not only the process but also its strengths and limitations. ... Brusco and Stahl do a nice job ... ." (Jon R. Kettenring, SIAM Review, Vol. 48 (2), 2006)

"This book both summarizes and illustrates the methods that are currently available for applying branch-and-bound methods to certain data analysis situations. ... The mathematical basis is clearly explained, pseudo-code for the algorithms is given where needed, very simple cases are used to demonstrate the methodology, and larger examples are also presented. ... Care is taken to discuss the limitations as well as the advantages of the branch-and-bound approach." (C.D. Kemp, Short Book Reviews, Vol. 26 (1), 2006)

"This book ... is the first attempt to use this technique, branch and bound, to solve discrete optimization problems that arise in statistical data analysis. The authors are one of the pioneers in this area and authored a series of papers in relevant leading journals. This book is ... written in a more lucid language, and includes a good number of examples, illustrations and case studies. ... This book is going to be extremely useful to the researchers or practitioners of combinatorialdata