LEADER 05307nam 2200673Ia 450 001 9910876922603321 005 20200520144314.0 010 $a1-282-12369-6 010 $a9786612123696 010 $a0-470-05886-2 010 $a0-470-74583-5 010 $a0-470-74582-7 035 $a(CKB)1000000000719673 035 $a(EBL)427914 035 $a(OCoLC)437111479 035 $a(SSID)ssj0000334784 035 $a(PQKBManifestationID)11256939 035 $a(PQKBTitleCode)TC0000334784 035 $a(PQKBWorkID)10270861 035 $a(PQKB)10703575 035 $a(MiAaPQ)EBC427914 035 $a(EXLCZ)991000000000719673 100 $a20090224d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApplied data mining for business and industry /$fPaolo Giudici, Silvia Figini 205 $a2nd ed. 210 $aHoboken, NJ $cJohn Wiley$d2009 215 $a1 online resource (259 p.) 300 $aDescription based upon print version of record. 311 $a0-470-05887-0 320 $aIncludes bibliographical references and index. 327 $aApplied Data Mining for Business and Industry; Contents; 1 Introduction; Part I Methodology; 2 Organisation of the data; 2.1 Statistical units and statistical variables; 2.2 Data matrices and their transformations; 2.3 Complex data structures; 2.4 Summary; 3 Summary statistics; 3.1 Univariate exploratory analysis; 3.1.1 Measures of location; 3.1.2 Measures of variability; 3.1.3 Measures of heterogeneity; 3.1.4 Measures of concentration; 3.1.5 Measures of asymmetry; 3.1.6 Measures of kurtosis; 3.2 Bivariate exploratory analysis of quantitative data 327 $a3.3 Multivariate exploratory analysis of quantitative data3.4 Multivariate exploratory analysis of qualitative data; 3.4.1 Independence and association; 3.4.2 Distance measures; 3.4.3 Dependency measures; 3.4.4 Model-based measures; 3.5 Reduction of dimensionality; 3.5.1 Interpretation of the principal components; 3.6 Further reading; 4 Model specification; 4.1 Measures of distance; 4.1.1 Euclidean distance; 4.1.2 Similarity measures; 4.1.3 Multidimensional scaling; 4.2 Cluster analysis; 4.2.1 Hierarchical methods; 4.2.2 Evaluation of hierarchical methods; 4.2.3 Non-hierarchical methods 327 $a4.3 Linear regression4.3.1 Bivariate linear regression; 4.3.2 Properties of the residuals; 4.3.3 Goodness of fit; 4.3.4 Multiple linear regression; 4.4 Logistic regression; 4.4.1 Interpretation of logistic regression; 4.4.2 Discriminant analysis; 4.5 Tree models; 4.5.1 Division criteria; 4.5.2 Pruning; 4.6 Neural networks; 4.6.1 Architecture of a neural network; 4.6.2 The multilayer perceptron; 4.6.3 Kohonen networks; 4.7 Nearest-neighbour models; 4.8 Local models; 4.8.1 Association rules; 4.8.2 Retrieval by content; 4.9 Uncertainty measures and inference; 4.9.1 Probability 327 $a4.9.2 Statistical models4.9.3 Statistical inference; 4.10 Non-parametric modelling; 4.11 The normal linear model; 4.11.1 Main inferential results; 4.12 Generalised linear models; 4.12.1 The exponential family; 4.12.2 Definition of generalised linear models; 4.12.3 The logistic regression model; 4.13 Log-linear models; 4.13.1 Construction of a log-linear model; 4.13.2 Interpretation of a log-linear model; 4.13.3 Graphical log-linear models; 4.13.4 Log-linear model comparison; 4.14 Graphical models; 4.14.1 Symmetric graphical models; 4.14.2 Recursive graphical models 327 $a4.14.3 Graphical models and neural networks4.15 Survival analysis models; 4.16 Further reading; 5 Model evaluation; 5.1 Criteria based on statistical tests; 5.1.1 Distance between statistical models; 5.1.2 Discrepancy of a statistical model; 5.1.3 Kullback-Leibler discrepancy; 5.2 Criteria based on scoring functions; 5.3 Bayesian criteria; 5.4 Computational criteria; 5.5 Criteria based on loss functions; 5.6 Further reading; Part II Business case studies; 6 Describing website visitors; 6.1 Objectives of the analysis; 6.2 Description of the data; 6.3 Exploratory analysis; 6.4 Model building 327 $a6.4.1 Cluster analysis 330 $aThe increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications.Cove 606 $aData mining 606 $aBusiness$xData processing 606 $aCommercial statistics 615 0$aData mining. 615 0$aBusiness$xData processing. 615 0$aCommercial statistics. 676 $a005.74068 676 $a006.312 700 $aGiudici$b Paolo$081878 701 $aFigini$b Silvia$01754032 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910876922603321 996 $aApplied data mining for business and industry$94190160 997 $aUNINA