000 04474nam a22005415i 4500
001 978-0-387-28810-9
003 DE-He213
005 20180115171356.0
007 cr nn 008mamaa
008 100301s2005 xxu| s |||| 0|eng d
020 _a9780387288109
024 7 _a10.1007/0-387-28810-4
050 4 _aQA150-272
072 7 _aPBD
072 7 _aMAT008000
082 0 4 _a511.1
100 1 _aBrusco, Michael J.
245 1 0 _aBranch-and-Bound Applications in Combinatorial Data Analysis
_h[electronic resource] /
_cby Michael J. Brusco, Stephanie Stahl.
264 1 _aNew York, NY :
_bSpringer New York,
300 _aXII, 222 p.
_bonline resource.
336 _atext
337 _acomputer
338 _aonline resource
347 _atext file
490 1 _aStatistics and Computing,
505 0 _aCluster 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.
520 _aThere 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.
650 0 _aMathematics.
650 0 _aOperations research.
650 0 _aDecision making.
650 0 _aManagement science.
650 0 _aDiscrete mathematics.
650 0 _aStatistics.
650 1 4 _aMathematics.
650 2 4 _aDiscrete Mathematics.
650 2 4 _aStatistics and Computing/Statistics Programs.
650 2 4 _aOperation Research/Decision Theory.
650 2 4 _aOperations Research, Management Science.
650 2 4 _aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
700 1 _aStahl, Stephanie.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
830 0 _aStatistics and Computing,
856 4 0 _uhttp://dx.doi.org/10.1007/0-387-28810-4
912 _aZDB-2-SMA
999 _c369370