Combinatorial data analysis:optimization by dynamic programming
作者： Lawrence Hubert，Phipps Arabie，Jacqueline Meulman著
出版社：清华大学出版社,2011
简介： 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.
Combinatorial Data Analysis: Optimization by Dynamic Programming
focuses 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 this book's
strongest points. The authors include verifiably optimal
solutions to nontrivially sized problems over the array of data
analysis tasks discussed.
This monograph provides an applied documentation source, as
well as an introduction to a collection of associated computer
programs, that will be of interest to applied statisticians and
data analysts as well as notationally sophisticated users.
