| Our purpose in writing this monograph is to provide an applied documen-tation source, as well as an introduction to a collection of associated computer programs that would be of interest to applied statisticians and data analysts but also accessible to a notationally sophisticated but otherwise substantively focused user. The content of the monograph itself and how the various parts are orga-nized can be discussed under a number of headings that serve to character-ize both the type of object arrangements to be identified and the form of the data on which the identification is to be based. |
| 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 of Ultrametrics 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 Matrices Based on the Construction of Optimal Paths 4.2 Sequencing an Object Set Subject to Precedence Constraints 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 SetsΩ1,...,Ωk 6.1.4 A Priori Weights for Objects and/or Proximities 6.2 Prospects Appendix: Available Programs Bibliography Author Index Subject Index |
内容加载中，请稍后...
商品评论（0条）