LEADER 05454nam 2200637Ia 4500001 99125348616806421 005 20240514045800.0 006 m o d | 007 cr -n--------- 008 100920s2011 enka ob 001 0 eng d 020 1-283-37397-1 020 9786613373977 020 0-470-97928-3 020 0-470-97916-X 020 0-470-97917-8 035 (CKB)3460000000000107 035 (EBL)792450 035 (OCoLC)716215543 035 (SSID)ssj0000476890 035 (PQKBManifestationID)11332018 035 (PQKBTitleCode)TC0000476890 035 (PQKBWorkID)10502069 035 (PQKB)10901146 035 (MiAaPQ)EBC792450 035 (Au-PeEL)EBL792450 035 (CaPaEBR)ebr10510552 035 (CaONFJC)MIL337397 035 (PPN)170223205 035 (EXLCZ)993460000000000107 040 MiAaPQ |cMiAaPQ |dMiAaPQ 041 eng 050 4 QA76.9.D343 |bT8413 2011 082 04 006.3/12 |222 100 1 Tuffery, Stéphane. 245 10 Data mining and statistics for decision making |h[electronic resource] / |cStéphane Tufféry; translated by Rod Riesco. 250 1st ed. 260 Chichester, West Sussex ;Hoboken, NJ. : |bWiley, |c2011. 300 1 online resource (717 p.) 336 text |btxt 337 computer |bc 338 online resource |bcr 490 1 Wiley series in computational statistics 500 Description based upon print version of record. 505 0 Data 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 505 8 2.4 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 505 8 3.5 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 505 8 3.15.1 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 505 8 5 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 505 8 7.1 Principal component analysis 520 Data 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 546 English 504 Includes bibliographical references and index. 650 0 Data mining. 650 0 Statistical decision. 776 |z0-470-68829-7 830 0 Wiley series in computational statistics. 906 BOOK