LEADER 03361nam 2200481 450 001 9910814409603321 005 20200311035446.0 010 $a1-119-01039-X 010 $a1-119-01038-1 010 $a1-119-01040-3 035 $a(CKB)4330000000008008 035 $a(MiAaPQ)EBC5850225 035 $a(CaSebORM)9780470713938 035 $a(EXLCZ)994330000000008008 100 $a20190916d2020 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aClustering methodology for symbolic data /$fLynne Billard (University of Georgia), Edwin Diday (Universite de Paris IX--Dauphine) 205 $a1st edition 210 1$aHoboken, New Jersey :$cWiley,$d[2020] 210 4$dİ2020 215 $a1 online resource (351 pages) 311 $a0-470-71393-3 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Symbolic data, basics -- Dissimilarity, similarity, and distance measures -- Dissimilarity, similarity, and distance measures, modal data -- General clustering techniques -- Partitioning techniques -- Divisive hierarchical clustering -- Agglomerative hierarchical clustering. 330 $aCovers everything readers need to know about clustering methodology for symbolic data?including new methods and headings?while providing a focus on multi-valued list data, interval data and histogram data This book presents all of the latest developments in the field of clustering methodology for symbolic data?paying special attention to the classification methodology for multi-valued list, interval-valued and histogram-valued data methodology, along with numerous worked examples. The book also offers an expansive discussion of data management techniques showing how to manage the large complex dataset into more manageable datasets ready for analyses. Filled with examples, tables, figures, and case studies, Clustering Methodology for Symbolic Data begins by offering chapters on data management, distance measures, general clustering techniques, partitioning, divisive clustering, and agglomerative and pyramid clustering. Provides new classification methodologies for histogram valued data reaching across many fields in data science Demonstrates how to manage a large complex dataset into manageable datasets ready for analysis Features very large contemporary datasets such as multi-valued list data, interval-valued data, and histogram-valued data Considers classification models by dynamical clustering Features a supporting website hosting relevant data sets Clustering Methodology for Symbolic Data will appeal to practitioners of symbolic data analysis, such as statisticians and economists within the public sectors. It will also be of interest to postgraduate students of, and researchers within, web mining, text mining and bioengineering. 606 $aCluster analysis 606 $aMultivariate analysis 615 0$aCluster analysis. 615 0$aMultivariate analysis. 676 $a519.53 700 $aBillard$b L$g(Lynne),$f1943-$0102384 702 $aDiday$b E. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910814409603321 996 $aClustering methodology for symbolic data$94127310 997 $aUNINA