LEADER 04658nam 2200649Ia 450 001 9910144714603321 005 20170810191530.0 010 $a1-280-59231-1 010 $a9786613622143 010 $a0-470-09018-9 010 $a0-470-09017-0 035 $a(CKB)1000000000377274 035 $a(EBL)792672 035 $a(OCoLC)793995917 035 $a(SSID)ssj0000354805 035 $a(PQKBManifestationID)11275387 035 $a(PQKBTitleCode)TC0000354805 035 $a(PQKBWorkID)10314689 035 $a(PQKB)11017180 035 $a(MiAaPQ)EBC792672 035 $a(EXLCZ)991000000000377274 100 $a20060825d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSymbolic data analysis$b[electronic resource] $econceptual statistics and data mining /$fLynne Billard, Edwin Diday 210 $aChichester, England ;$aHoboken, NJ $cJohn Wiley & Sons Inc.$dc2006 215 $a1 online resource (331 p.) 225 1 $aWiley Series in Computational Statistics ;$vv.654 225 0$aWiley series in computational statistics 300 $aDescription based upon print version of record. 311 $a0-470-09016-2 320 $aIncludes bibliographical references and indexes. 327 $aSymbolic Data Analysis; Contents; 1 Introduction; References; 2 Symbolic Data; 2.1 Symbolic and Classical Data; 2.1.1 Types of data; 2.1.2 Dependencies in the data; 2.2 Categories, Concepts, and Symbolic Objects; 2.2.1 Preliminaries; 2.2.2 Descriptions, assertions, extents; 2.2.3 Concepts of concepts; 2.2.4 Some philosophical aspects; 2.2.5 Fuzzy, imprecise, and conjunctive data; 2.3 Comparison of Symbolic and Classical Analyses; Exercises; References; 3 Basic Descriptive Statistics: One Variate; 3.1 Some Preliminaries; 3.2 Multi-Valued Variables; 3.3 Interval-Valued Variables 327 $a3.4 Modal Multi-Valued Variables3.5 Modal Interval-Valued Variables; Exercises; References; 4 Descriptive Statistics: Two or More Variates; 4.1 Multi-Valued Variables; 4.2 Interval-Valued Variables; 4.3 Modal Multi-Valued Variables; 4.4 Modal Interval-Valued Variables; 4.5 Baseball Interval-Valued Dataset; 4.5.1 The data: actual and virtual datasets; 4.5.2 Joint histograms; 4.5.3 Guiding principles; 4.6 Measures of Dependence; 4.6.1 Moment dependence; 4.6.2 Spearman's rho and copulas; Exercises; References; 5 Principal Component Analysis; 5.1 Vertices Method; 5.2 Centers Method 327 $a5.3 Comparison of the MethodsExercises; References; 6 Regression Analysis; 6.1 Classical Multiple Regression Model; 6.2 Multi-Valued Variables; 6.2.1 Single dependent variable; 6.2.2 Multi-valued dependent variable; 6.3 Interval-Valued Variables; 6.4 Histogram-Valued Variables; 6.5 Taxonomy Variables; 6.6 Hierarchical Variables; Exercises; References; 7 Cluster Analysis; 7.1 Dissimilarity and Distance Measures; 7.1.1 Basic definitions; 7.1.2 Multi-valued variables; 7.1.3 Interval-valued variables; 7.1.4 Mixed-valued variables; 7.2 Clustering Structures; 7.2.1 Types of clusters: definitions 327 $a7.2.2 Construction of clusters: building algorithms7.3 Partitions; 7.4 Hierarchy-Divisive Clustering; 7.4.1 Some basics; 7.4.2 Multi-valued variables; 7.4.3 Interval-valued variables; 7.5 Hierarchy-Pyramid Clusters; 7.5.1 Some basics; 7.5.2 Comparison of hierarchy and pyramid structures; 7.5.3 Construction of pyramids; Exercises; References; Data Index; Author Index; Subject Index 330 $aWith the advent of computers, very large datasets have become routine. Standard statistical methods don't have the power or flexibility to analyse these efficiently, and extract the required knowledge. An alternative approach is to summarize a large dataset in such a way that the resulting summary dataset is of a manageable size and yet retains as much of the knowledge in the original dataset as possible. One consequence of this is that the data may no longer be formatted as single values, but be represented by lists, intervals, distributions, etc. The summarized data have their own internal s 410 0$aWiley Series in Computational Statistics 606 $aData mining 606 $aMultivariate analysis 608 $aElectronic books. 615 0$aData mining. 615 0$aMultivariate analysis. 676 $a519.5/35 676 $a519.535 700 $aBillard$b L$g(Lynne),$f1943-$0102384 701 $aDiday$b E$0860583 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910144714603321 996 $aSymbolic data analysis$92245924 997 $aUNINA