the authors provide a comprehensive and self-contained reviewdelineating a very general dp paradigm, or schema, that can servetwo functions. first, the paradigm can be applied in variousspecial forms to encompass all previously proposed applicationssuggested in the classification literature. second, the paradigmcan lead directly to many more novel uses. an appendix is includedas a user's manual for a collection of programs available asfreeware.
the incorporation of a wide variety of cda tasks under one commonoptimization framework based on dp is one of this book's strongestpoints. the authors include verifiably optimal solutions tonontrivially sized problems over the array of data analysis tasksdiscussed.
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.