LEADER 05580nam 2200709Ia 450 001 9910130875003321 005 20240514045800.0 010 $a1-283-37397-1 010 $a9786613373977 010 $a0-470-97928-3 010 $a0-470-97916-X 010 $a0-470-97917-8 035 $a(CKB)3460000000000107 035 $a(EBL)792450 035 $a(SSID)ssj0000476890 035 $a(PQKBManifestationID)11332018 035 $a(PQKBTitleCode)TC0000476890 035 $a(PQKBWorkID)10502069 035 $a(PQKB)10901146 035 $a(Au-PeEL)EBL792450 035 $a(CaPaEBR)ebr10510552 035 $a(CaONFJC)MIL337397 035 $a(PPN)170223205 035 $a(OCoLC)711780360 035 $a(FR-PaCSA)88803180 035 $a(MiAaPQ)EBC792450 035 $a(EXLCZ)993460000000000107 100 $a20100920d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData mining and statistics for decision making /$fSte?phane Tuffe?ry; translated by Rod Riesco 205 $a1st ed. 210 $aChichester, West Sussex ;$aHoboken, NJ. $cWiley$d2011 215 $a1 online resource (717 p.) 225 1 $aWiley series in computational statistics 300 $aDescription based upon print version of record. 311 $a0-470-68829-7 320 $aIncludes bibliographical references and index. 327 $aData Mining and Statistics for Decision Making; Contents; Preface; Foreword; Foreword from the French language edition; List of trademarks; 1 Overview of data mining; 1.1 What is data mining?; 1.2 What is data mining used for?; 1.2.1 Data mining in different sectors; 1.2.2 Data mining in different applications; 1.3 Data mining and statistics; 1.4 Data mining and information technology; 1.5 Data mining and protection of personal data; 1.6 Implementation of data mining; 2 The development of a data mining study; 2.1 Defining the aims; 2.2 Listing the existing data; 2.3 Collecting the data 327 $a2.4 Exploring and preparing the data2.5 Population segmentation; 2.6 Drawing up and validating predictive models; 2.7 Synthesizing predictive models of different segments; 2.8 Iteration of the preceding steps; 2.9 Deploying the models; 2.10 Training the model users; 2.11 Monitoring the models; 2.12 Enriching the models; 2.13 Remarks; 2.14 Life cycle of a model; 2.15 Costs of a pilot project; 3 Data exploration and preparation; 3.1 The different types of data; 3.2 Examining the distribution of variables; 3.3 Detection of rare or missing values; 3.4 Detection of aberrant values 327 $a3.5 Detection of extreme values3.6 Tests of normality; 3.7 Homoscedasticity and heteroscedasticity; 3.8 Detection of the most discriminating variables; 3.8.1 Qualitative, discrete or binned independent variables; 3.8.2 Continuous independent variables; 3.8.3 Details of single-factor non-parametric tests; 3.8.4 ODS and automated selection of discriminating variables; 3.9 Transformation of variables; 3.10 Choosing ranges of values of binned variables; 3.11 Creating new variables; 3.12 Detecting interactions; 3.13 Automatic variable selection; 3.14 Detection of collinearity; 3.15 Sampling 327 $a3.15.1 Using sampling3.15.2 Random sampling methods; 4 Using commercial data; 4.1 Data used in commercial applications; 4.1.1 Data on transactions and RFM Data; 4.1.2 Data on products and contracts; 4.1.3 Lifetimes; 4.1.4 Data on channels; 4.1.5 Relational, attitudinal and psychographic data; 4.1.6 Sociodemographic data; 4.1.7 When data are unavailable; 4.1.8 Technical data; 4.2 Special data; 4.2.1 Geodemographic data; 4.2.2 Profitability; 4.3 Data used by business sector; 4.3.1 Data used in banking; 4.3.2 Data used in insurance; 4.3.3 Data used in telephony; 4.3.4 Data used in mail order 327 $a5 Statistical and data mining software5.1 Types of data mining and statistical software; 5.2 Essential characteristics of the software; 5.2.1 Points of comparison; 5.2.2 Methods implemented; 5.2.3 Data preparation functions; 5.2.4 Other functions; 5.2.5 Technical characteristics; 5.3 The main software packages; 5.3.1 Overview; 5.3.2 IBM SPSS; 5.3.3 SAS; 5.3.4 R; 5.3.5 Some elements of the R language; 5.4 Comparison of R, SAS and IBM SPSS; 5.5 How to reduce processing time; 6 An outline of data mining methods; 6.1 Classification of the methods; 6.2 Comparison of the methods; 7 Factor analysis 327 $a7.1 Principal component analysis 330 $aData mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized l 410 0$aWiley series in computational statistics. 606 $aData mining 606 $aStatistical decision 615 0$aData mining. 615 0$aStatistical decision. 676 $a006.3/12 700 $aTuffery$b Ste?phane$0424510 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910130875003321 996 $aData mining and statistics for decision making$9835535 997 $aUNINA