E Book

Branch-and-Bound Applications in Combinatorial Data Analysis

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Cluster Analysis—Partitioning -- An Introduction to Branch-and-Bound Methods for Partitioning -- Minimum-Diameter Partitioning -- Minimum Within-Cluster Sums of Dissimilarities Partitioning -- Minimum Within-Cluster Sums of Squares Partitioning -- Multiobjective Partitioning -- Seriation -- to the Branch-and-Bound Paradigm for Seriation -- Seriation—Maximization of a Dominance Index -- Seriation—Maximization of Gradient Indices -- Seriation—Unidimensional Scaling -- Seriation—Multiobjective Seriation -- Variable Selection -- to Branch-and-Bound Methods for Variable Selection -- Variable Selection for Cluster Analysis -- Variable Selection for Regression Analysis.There are a variety of combinatorial optimization problems that are relevant to the examination of statistical data. Combinatorial problems arise in the clustering of a collection of objects, the seriation (sequencing or ordering) of objects, and the selection of variables for subsequent multivariate statistical analysis such as regression. The options for choosing a solution strategy in combinatorial data analysis can be overwhelming. Because some problems are too large or intractable for an optimal solution strategy, many researchers develop an over-reliance on heuristic methods to solve all combinatorial problems. However, with increasingly accessible computer power and ever-improving methodologies, optimal solution strategies have gained popularity for their ability to reduce unnecessary uncertainty. In this monograph, optimality is attained for nontrivially sized problems via the branch-and-bound paradigm. For many combinatorial problems, branch-and-bound approaches have been proposed and/or developed. However, until now, there has not been a single resource in statistical data analysis to summarize and illustrate available methods for applying the branch-and-bound process. This monograph provides clear explanatory text, illustrative mathematics and algorithms, demonstrations of the iterative process, psuedocode, and well-developed examples for applications of the branch-and-bound paradigm to important problems in combinatorial data analysis. Supplementary material, such as computer programs, are provided on the world wide web. Dr. Brusco is a Professor of Marketing and Operations Research at Florida State University, an editorial board member for the Journal of Classification, and a member of the Board of Directors for the Classification Society of North America. Stephanie Stahl is an author and researcher with years of experience in writing, editing, and quantitative psychology research.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

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Penerbit

New York : Springer., 2005

Deskripsi Fisik

XII, 222 p.online resource.

Bahasa

English

ISBN/ISSN

9780387288109

Klasifikasi

511.1

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

1st ed. 2005.

Subjek

Statistics .
Statistics and Computing/Statistics Programs.
Management science.
Operations research.
Decision making.
Operations Research/Decision Theory.
Operations Research, Management Science.
Discrete mathematics.
Statistics for Social Sciences, Humanities, Law.

Info Detail Spesifik

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Pernyataan Tanggungjawab

Michael J. Brusco, Stephanie Stahl.

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