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Branch-and-Bound Applications in Combinatorial Data Analysis

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Cover of 'Branch-and-Bound Applications in Combinatorial Data Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 An Introduction to Branch-and-Bound Methods for Partitioning
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    Chapter 3 Minimum-Diameter Partitioning
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    Chapter 4 Minimum Within-Cluster Sums of Dissimilarities Partitioning
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    Chapter 5 Minimum Within-Cluster Sums of Squares Partitioning
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    Chapter 6 Multiobjective Partitioning
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    Chapter 7 Introduction to the Branch-and-Bound Paradigm for Seriation
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    Chapter 8 Seriation—Maximization of a Dominance Index
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    Chapter 9 Seriation—Maximization of Gradient Indices
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    Chapter 10 Seriation—Unidimensional Scaling
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    Chapter 11 Seriation—Multiobjective Seriation
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    Chapter 12 Introduction to Branch-and-Bound Methods for Variable Selection
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    Chapter 13 Variable Selection for Cluster Analysis
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    Chapter 14 Variable Selection for Regression Analysis
Attention for Chapter 4: Minimum Within-Cluster Sums of Dissimilarities Partitioning
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