Combinatorial Data Analysis: Optimization by Dynamic Programming (Monographs on Discrete Mathematics and Applications, Series Number 6)

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.

Lawrence Hubert is Professor of Psychology and Statistics at the University of Illinois at Urbana-Champaign, USA; Phipps Arabie is Professor of Management and Psychology at Rutgers University, USA; Jacqueline Meulman is Professor of Applied Data Theory in the Faculty of Social and Behavioral Sciences of Leiden University, The Netherlands.