LEADER 03769nam 2200577 a 450 001 9910144713103321 005 20170815112922.0 010 $a1-281-30833-1 010 $a9786611308339 010 $a0-470-72356-4 010 $a0-470-72355-6 035 $a(CKB)1000000000377278 035 $a(EBL)351480 035 $a(OCoLC)476172445 035 $a(SSID)ssj0000254659 035 $a(PQKBManifestationID)11209328 035 $a(PQKBTitleCode)TC0000254659 035 $a(PQKBWorkID)10208472 035 $a(PQKB)11531289 035 $a(MiAaPQ)EBC351480 035 $a(EXLCZ)991000000000377278 100 $a20071105d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aSymbolic data analysis and the SODAS software$b[electronic resource] /$fedited by Edwin Diday, Monique Noirhomme-Fraiture 210 $aChichester, England ;$aHoboken, NJ $cJ. Wiley & Sons$dc2008 215 $a1 online resource (477 p.) 300 $aDescription based upon print version of record. 311 $a0-470-01883-6 320 $aIncludes bibliographical references and index. 327 $aSymbolic Data Analysis and the SODAS Software; Contents; Contributors; Foreword; Preface; ASSO Partners; Introduction; 1 The state of the art in symbolic data analysis: overview and future; Part I Databases versus Symbolic Objects; 2 Improved generation of symbolic objects from relational databases; 3 Exporting symbolic objects to databases; 4 A statistical metadata model for symbolic objects; 5 Editing symbolic data; 6 The normal symbolic form; 7 Visualization; Part II Unsupervised Methods; 8 Dissimilarity and matching; 9 Unsupervised divisive classification 327 $a10 Hierarchical and pyramidal clustering11 Clustering methods in symbolic data analysis; 12 Visualizing symbolic data by Kohonen maps; 13 Validation of clustering structure: determination of the number of clusters; 14 Stability measures for assessing a partition and its clusters: application to symbolic data sets; 15 Principal component analysis of symbolic data described by intervals; 16 Generalized canonical analysis; Part III Supervised Methods; 17 Bayesian decision trees; 18 Factor discriminant analysis; 19 Symbolic linear regression methodology 327 $a20 Multi-layer perceptrons and symbolic dataPart IV Applications and the SODAS Software; 21 Application to the Finnish, Spanish and Portuguese data of the European Social Survey; 22 People's life values and trust components in Europe: symbolic data analysis for 20-22 countries; 23 Symbolic analysis of the Time Use Survey in the Basque country; 24 SODAS2 software: Overview and methodology; Index 330 $aSymbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of very large datasets, and symbolic data analysis techniques have been developed in order to extract knowledge from such data. Symbolic data methods differ from that of data mining, for example, because rather than identifying points of interest in the data, symbolic data methods allow the user to build models of the data and make predictions about future events.This book is the result of the work 606 $aData mining 608 $aElectronic books. 615 0$aData mining. 676 $a005.74 676 $a519.535 701 $aDiday$b E$0860583 701 $aNoirhomme-Fraiture$b Monique$0856348 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910144713103321 996 $aSymbolic data analysis and the SODAS software$91920443 997 $aUNINA