组合数据分析：通过动态规划进行优化（英文影印版）
出版时间：2011年版
内容简介
　　combinatorial data analysis （cda） refers to a wide class ofmethods for the study of relevant data sets in which thearrangement of a collection of objects is absolutely central.combinatorial data analysis： optimization by dynamic programmingfocuses on the identification of arrangements， which are thenfurther restricted to where the combinatorial search is carried outby a recursive optimization process based on the general principlesof dynamic programming （dp）.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.this monograph provides an applied documentation source， as well asan introduction to a collection of associated computer programs，that will be of interest to applied statisticians and data analystsas well as notationally sophisticated users.
目录
preface
1 introduction
2 general dynamic programming paradigm
　2.1 an introductory example: linear assignment
　2.2 the gdpp
3 cluster analysis
　3.1 partitioning
　3.1.1 admissibility restrictions on partitions
　3.1.2 partitioning based on two-mode proximity matrices
　3.2 hierarchical clustering
　3.2.1 hierarchical clustering and the optimal fitting ofultrametrics
　3.2.2 constrained hierarchical clustering
4 object sequencing and seriation
　4.1 optimal sequencing of a single object set
　4.1.1 symmetric one-mode proximity matrices
　4.1.2 skew-symmetric one-mode proximity matrices
　4.1.3 two-mode proximity matrices
　4.1.4 object sequencing for symmetric one-mode proximity matricesbased on the construction of optimal paths
　4.2 sequencing an object set subject to precedenceconstraints
　4.3 construction of optimal ordered partitions
5 heuristic applications of the gdpp
　5.1 cluster analysis
　5.2 object sequencing and seriation
6 extensions and generalizations
　6.1 introduction
　6.1.1 multiple data sources
　6.1.2 multiple structures
　6.1.3 uses for the information in the sets ω1,...,ωk
　6.1.4 a priori weights for objects and/or proximities
　6.2 prospects
appendix: available programs
bibliography
author index
subject index