圖書信息
出版社: 清華大學出版社; 第1版 (2011年2月1日)
外文書名: Combinatorial Data Analysis:Optimization by Dynamic Programming
叢書名: 國際著名數學圖書·影印版
平裝: 163頁
正文語種: 英語
開本: 16
ISBN: 7302245010, 9787302245018
條形碼: 9787302245018
尺寸: 24.2 x 17.2 x 1 cm
重量: 240 g
作者簡介
作者：（美國）休伯特（Lawrence Hubert） （美國）Phipps Arabie （美國）Jacqueline Meulman
內容簡介
《組合數據分析:通過動態規划進行最佳化》內容簡介：Combinatorial data analysis(CDA)refe rs to a wide class of methods for the study of relevant data sets in which the ar rangement of a collection of objects is absolutely central．Combinatorial Data Analysis：Optimization by Dynamic Programming focu ses on the identification of a rrangements，which a re then fu rther rest ricted to whe re the combinatorial sea rch is ca r ried out by a recu rsive optimization p rocess based on the gene ral principles of dynamic p rog ramming(DP)． The authors provide a comprehen sive and self-contained review delineating a very general DP pa radigm，o r schema，that can se rve two functions．Fi rst，the pa radigm can be applied in va riou s special form s to encompass all previously p roposed applications suggested in the classification lite rature．Second，the pa radigm can lead directly to many mo re novel uses．An appendix is included as a user’s manual for a collection of prog ram s available as Freeware．
目錄
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 ofOptimal 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
6．1．4 A Priori Weights for Objects and/or Proximities
6．2 Prospects
Appendix：Available Programs
Bibliography
Author Index
Subject Index
