04503nam a22005415i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003100137050001400168072001600182072002300198082001400221100003200235245013000267264004600397300003400443336002600477337002600503338003600529347002400565490004100589505068600630520193201316650001703248650002503265650002103290650002403311650002603335650001603361650001703377650002603394650005003420650004003470650004503510650009003555700003103645710003403676773002003710776003603730830004103766856004403807912001403851999001903865952007703884978-0-387-28810-9DE-He21320180115171356.0cr nn 008mamaa100301s2005 xxu| s |||| 0|eng d a97803872881099978-0-387-28810-97 a10.1007/0-387-28810-42doi 4aQA150-272 7aPBD2bicssc 7aMAT0080002bisacsh04a511.12231 aBrusco, Michael J.eauthor.10aBranch-and-Bound Applications in Combinatorial Data Analysish[electronic resource] /cby Michael J. Brusco, Stephanie Stahl. 1aNew York, NY :bSpringer New York,c2005. aXII, 222 p.bonline resource. atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier atext filebPDF2rda1 aStatistics and Computing,x1431-87840 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. 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. 0aMathematics. 0aOperations research. 0aDecision making. 0aManagement science. 0aDiscrete mathematics. 0aStatistics.14aMathematics.24aDiscrete Mathematics.24aStatistics and Computing/Statistics Programs.24aOperation Research/Decision Theory.24aOperations Research, Management Science.24aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.1 aStahl, Stephanie.eauthor.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387250373 0aStatistics and Computing,x1431-878440uhttp://dx.doi.org/10.1007/0-387-28810-4 aZDB-2-SMA c369370d369370 001040708EBookaelibbelibd2018-01-15r2018-01-15w2018-01-15yEBOOK