LEADER 06017nam 22006975 450 001 9910299987103321 005 20200701145817.0 010 $a1-4939-0742-5 024 7 $a10.1007/978-1-4939-0742-7 035 $a(CKB)3710000000128890 035 $a(EBL)1782026 035 $a(SSID)ssj0001274999 035 $a(PQKBManifestationID)11856945 035 $a(PQKBTitleCode)TC0001274999 035 $a(PQKBWorkID)11338088 035 $a(PQKB)11337730 035 $a(MiAaPQ)EBC1782026 035 $a(DE-He213)978-1-4939-0742-7 035 $a(PPN)179767496 035 $a(EXLCZ)993710000000128890 100 $a20140611d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aClusters, Orders, and Trees: Methods and Applications $eIn Honor of Boris Mirkin's 70th Birthday /$fedited by Fuad Aleskerov, Boris Goldengorin, Panos M. Pardalos 205 $a1st ed. 2014. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2014. 215 $a1 online resource (404 p.) 225 1 $aSpringer Optimization and Its Applications,$x1931-6828 ;$v92 300 $aDescription based upon print version of record. 311 $a1-322-13290-9 311 $a1-4939-0741-7 320 $aIncludes bibliographical references. 327 $aThree and One Questions to Dr. B. Mirkin about Complexity Statistics (I. Mandel) -- Section 1 -- A Polynomial Algorithm for a Class of 0-1 Fractional Programming Problems Involving Composite Functions, with an Application to Additive Clustering (P. Hansen, C. Meyer) -- Experiments with a Non-Convex Variance-Based Clustering Criterion (R.F. Toso, E.V. Bauman, C.A. Kulikowski, I.B. Muchnik) -- Strategy-Proof Location Functions on Finite Graphs (F.R. McMorris, H.M. Mulder, F.S. Roberts) -- A Pseudo-Boolean Approach to the Market Graph Analysis by Means of the p-Median Model (B. Goldengorin, A. Kocheturov, P.M. Pardalos) -- Clustering as an Approach to 3D Reconstruction Problems (S. Archangelski, I. Muchnik) -- Selecting the Minkowski Exponent for Intelligent K-Means with Feature Weighting (R. Cordeiro de Amorim, B. Mirkin) -- High Dimensional Data Classification (V. Pappu, P.M. Pardalos) -- Algorithm FRiS-TDR for Generalized Classification of the Labeled, Semi-Labeled and Unlabeled Datasets (I.A. Borisova, N.G. Zagoruiko) -- From Separating to Proximal Plane Classifiers: A Review (M.B. Ferraro, M.R. Guarracino) -- A Note on the Effectiveness of the Least Squares Consensus Clustering (B. Mirkin, A. Shestakov) -- Section 2 -- Single or Multiple Consensus for Linear Orders (A. Guenoche) -- Choice Functions on Tree Quasi-Orders (R.C. Powers, F. McMorris) -- Weak Hierarchies: a Central Clustering Structure (P. Bertrand, J. Diatta) -- Some Observations on Oligarchies, Internal Direct Sums and Lattice Congruences (M.F. Janowitz) -- Thinking Ultrametrically, Thinking p-Adically (F. Murtagh) -- A New Algorithm for Inferring Hybridization Events Based on the Detection of Horizontal Gene Transfers (V. Makarenkov, A. Box, P. Legendre) -- Section 3 -- Meaningless Statements in Landscape Ecology and Sustainable Environments (F. Roberts) -- Nearest Neighbour in Least Squares Data Imputation Algorithms for Marketing Data (I. Wasito). An AST Method for Scoring String-to-Text Similarity in Semantic Text Analysis (K. Chernyak, B. Mirkin) -- Improving Web Search Relevance with Learning Structure of Domain Concepts (B. Galitsky, B. Kovalerchuk) -- Linear Regression via Elastic Net: Non-Enumerative Leave-One-Out Verification of Feature Selection (E. Chernousova, N. Razin, O. Krasotkina, V. Mottl, D. Windridge) -- The Manipulability Index in the IANC Model (Y. Veselova). 330 $aThe volume is dedicated to Boris Mirkin on the occasion of his 70th birthday. In addition to his startling PhD results in abstract automata theory, Mirkin?s ground breaking contributions in various fields of decision making and data analysis have marked the fourth quarter of the 20th century and beyond. Mirkin has done pioneering work in group choice, clustering, data mining and knowledge discovery aimed at finding and describing non-trivial or hidden structures?first of all, clusters, orderings, and hierarchies?in multivariate and/or network data. This volume contains a collection of papers reflecting recent developments rooted in Mirkin's fundamental contribution to the state-of-the-art in group choice, ordering, clustering, data mining, and knowledge discovery. Researchers, students, and software engineers will benefit from new knowledge discovery techniques and application directions. 410 0$aSpringer Optimization and Its Applications,$x1931-6828 ;$v92 606 $aComputer science?Mathematics 606 $aComputer mathematics 606 $aArtificial intelligence 606 $aMathematical Applications in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M13110 606 $aDiscrete Mathematics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17028 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputer science?Mathematics. 615 0$aComputer mathematics. 615 0$aArtificial intelligence. 615 14$aMathematical Applications in Computer Science. 615 24$aDiscrete Mathematics in Computer Science. 615 24$aArtificial Intelligence. 676 $a004.015113 702 $aAleskerov$b Fuad$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGoldengorin$b Boris$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPardalos$b Panos M$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299987103321 996 $aClusters, Orders, and Trees: Methods and Applications$92544388 997 $aUNINA