000			10251cam a2200841Ia 4500
001			ocn716215543
003			OCoLC
005			20171119091211.0
006			m o d
007			cr cn|||||||||
008			110428s2011 enka ob 001 0 eng d
020			_a9780470979174 _q(electronic bk.)
020			_a0470979178 _q(electronic bk.)
020			_a9780470979167 _q(electronic bk.)
020			_a047097916X _q(electronic bk.)
020			_a1283373971
020			_a9781283373975
020			_z9780470979280
020			_z0470979283
020			_z9780470688298 _q(hardback)
020			_z0470688297
024	8		_a9786613373977
029	1		_aAU@ _b000051576698
029	1		_aCHNEW _b000720557
029	1		_aDEBBG _bBV041908537
029	1		_aDEBSZ _b372823890
029	1		_aDEBSZ _b397155158
029	1		_aDEBSZ _b431052387
029	1		_aDEBSZ _b449264181
029	1		_aDKDLA _b820120-katalog:000556369
029	1		_aHEBIS _b299832619
029	1		_aNZ1 _b13876188
029	1		_aNZ1 _b14257160
029	1		_aNZ1 _b15290871
029	1		_aDEBBG _bBV043393085
035			_a(OCoLC)716215543 _z(OCoLC)711780360 _z(OCoLC)765144014 _z(OCoLC)769189252 _z(OCoLC)769849270 _z(OCoLC)771999468 _z(OCoLC)772397870 _z(OCoLC)816879070 _z(OCoLC)961503244 _z(OCoLC)961597673 _z(OCoLC)962613432 _z(OCoLC)962729284
037			_a10.1002/9780470979174 _bWiley InterScience _nhttp://www3.interscience.wiley.com
040			_aUIU _beng _epn _cUIU _dYDXCP _dDG1 _dOCLCQ _dCOO _dB24X7 _dE7B _dCDX _dOCLCQ _dREDDC _dOCLCQ _dEBLCP _dDEBSZ _dOCLCO _dOCLCQ _dN$T _dOCLCF _dOCLCQ _dIDEBK _dDEBBG _dOCLCQ _dS3O _dOCLCQ _dAZK
041	1		_aeng _hfre
049			_aMAIN
050		4	_aQA76.9.D343 _bT84 2011
072		7	_aCOM _x021030 _2bisacsh
072		7	_aPBT _2bicssc
082	0	4	_a006.3/12 _222
100	1		_aTuffery, Stephane.
245	1	0	_aData mining and statistics for decision making / _cStephane Tuffery. _h[electronic resource]
260			_aChichester, West Sussex ; _aHoboken, NJ. : _bWiley, _c2011.
300			_a1 online resource (xxiv, 689 pages) : _billustrations.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_adata file _2rda
380			_aBibliography
490	1		_aWiley series in computational statistics
504			_aIncludes bibliographical references and index.
505	0		_aFront Matter -- Overview of Data Mining -- The Development of a Data Mining Study -- Data Exploration and Preparation -- Using Commercial Data -- Statistical and Data Mining Software -- An Outline of Data Mining Methods -- Factor Analysis -- Neural Networks -- Cluster Analysis -- Association Analysis -- Classification and Prediction Methods -- An Application of Data Mining: Scoring -- Factors for Success in a Data Mining Project -- Text Mining -- Web Mining -- Appendix A: Elements of Statistics -- Appendix B: Further Reading -- Index.
505	8		_aMachine generated contents note: Preface -- Foreword -- Contents -- Overview of data mining -- 1.1. What is data mining? -- 1.2. What is data mining used for? -- 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 -- The development of a data mining study -- 2.1. Defining the aims -- 2.2. Listing the existing data -- 2.3. Collecting the data -- 2.4. Exploring and preparing the data -- 2.5. 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 -- 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 -- 3.5. Detection of extreme values -- 3.6. Tests of normality -- 3.7. Homoscedasticity and heteroscedasticity -- 3.8. Detection of the most discriminating variables -- 3.9. Transformation of variables -- 3.10. Choosing ranges of values of continuous variables -- 3.11. Creating new variables -- 3.12. Detecting interactions 89 -- 3.13. Automatic variable selection -- 3.14. Detection of collinearity -- 3.15. Sampling -- Using commercial data -- 4.1. Data used in commercial applications -- 4.2. Special data -- 4.3. Data used by business sector -- Statistical and data mining software -- 5.1. Types of data mining and statistical software -- 5.2. Essential characteristics of the software -- 5.3. The main software packages -- 5.4. Comparison of R, SAS and IBM SPSS -- 5.5. How to reduce processing time -- An outline of data mining methods -- 6.1. A note on terminology -- 6.2. Classification of the methods -- 6.3. Comparison of the methods -- 6.4. Using these methods in the business world -- Factor analysis -- 7.1. Principal component analysis -- 7.2. Variants of principal component analysis -- 7.3. Correspondence analysis -- 7.4. Multiple correspondence analysis -- Neural networks -- 8.1. General information on neural networks -- 8.2. Structure of a neural network -- 8.3. Choosing the training sample -- 8.4. Some empirical rules for network design -- 8.5. Data normalization -- 8.6. Learning algorithms -- 8.7. The main neural networks -- Automatic clustering methods -- 9.1. Definition of clustering -- 9.2. Applications of clustering -- 9.3. Complexity of clustering -- 9.4. Clustering structures -- 9.5. Some methodological considerations -- 9.6. Comparison of factor analysis and clustering -- 9.7. Intra-class and inter-class inertias -- 9.8. Measurements of clustering quality -- 9.9. Partitioning methods -- 9.10. Hierarchical ascending clustering -- 9.11. Hybrid clustering methods -- 9.12. Neural clustering -- 9.13. Clustering by aggregation of similarities -- 9.14. Clustering of numeric variables -- 9.15. Overview of clustering methods -- Finding associations -- 10.1. Principles -- 10.2. Using taxonomy -- 10.3. Using supplementary variables -- 10.4. Applications -- 10.5. Example of use -- Classification and prediction methods -- 11.1. Introduction -- 11.2. Inductive and transductive methods -- 11.3. Overview of classification and prediction methods -- 11.4. Classification by decision tree -- 11.5. Prediction by decision tree -- 11.6. Classification by discriminant analysis -- 11.7. Prediction by linear regression -- 11.8. Classification by logistic regression -- 11.9. Developments in logistic regression -- 11.10. Bayesian methods -- 11.11. Classification and prediction by neural networks -- 11.12. Classification by support vector machines (SVMs) -- 11.13. Prediction by genetic algorithms -- 11.14. Improving the performance of a predictive model -- 11.15. Bootstrapping and aggregation of models -- 11.16. Using classification and prediction methods -- An application of data mining: scoring -- 12.1. The different types of score -- 12.2. Using propensity scores and risk scores -- 12.3. Methodology -- 12.4. Implementing a strategic score -- 12.5. Implementing an operational score -- 12.6. The kinds of scoring solutions used in a business -- 12.7. An example of credit scoring (data preparation) -- 12.8. An example of credit scoring (modelling by logistic regression) -- 12.9. An example of credit scoring (modelling by DISQUAL discriminant analysis) -- 12.10. A brief history of credit scoring -- Factors for success in a data mining project -- 13.1. The subject -- 13.2. The people -- 13.3. The data -- 13.4. The IT systems -- 13.5. The business culture -- 13.6. Data mining: eight common misconceptions -- 13.7. Return on investment -- Text mining -- 14.1. Definition of text mining -- 14.2. Text sources used -- 14.3. Using text mining -- 14.4. Information retrieval -- 14.5. Information extraction -- 14.6. Multi-type data mining -- Web mining -- 15.1. The aims of web mining -- 15.2. Global analyses -- 15.3. Individual analyses -- 15.4. Personal analyses -- Appendix: Elements of statistics -- 16.1. A brief history -- 16.2. Elements of statistics -- 16.3. Statistical tables -- Further reading -- 17.1. Statistics and data analysis -- 17.2. Data mining and statistical learning -- 17.3. Text mining -- 17.4. Web mining -- 17.5. R software -- 17.6. SAS software -- 17.7. IBM SPSS software -- 17.8. Websites -- Index.
520			_a"This practical guide to understanding and implementing data mining techniques discusses traditional methods--cluster analysis, factor analysis, linear regression, PLS regression, and generalized linear models--and recent methods--bagging and boosting, decision trees, neural networks, support vector machines, and genetic algorithm. The book focuses largely on credit scoring, one of the most common applications of predictive techniques, but also includes other descriptive techniques, such as customer segmentation. It also covers data mining with R, provides a comparison of SAS and SPSS, and includes an appendix presenting the necessary statistical background"-- _cProvided by publisher.
520			_a"Data Mining is a practical guide to understanding and implementing data mining techniques, featuring traditional methods such as cluster analysis, factor analysis, linear regression, PLS regression and generalised linear models"-- _cProvided by publisher.
588	0		_aPrint version record.
650		0	_aData mining.
650		0	_aStatistical decision.
650		4	_aData mining.
650		4	_aStatistical decision.
650		7	_aCOMPUTERS _xDatabase Management _xData Mining. _2bisacsh
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
650		7	_aStatistical decision. _2fast _0(OCoLC)fst01132059
655		4	_aElectronic books.
776	0	8	_iPrint version: _aTuffery, Stephane. _tData mining and statistics for decision making. _dChichester, West Sussex ; Hoboken, NJ. : Wiley, 2011 _z9780470688298 _w(DLC) 2010039789 _w(OCoLC)669160723
830		0	_aWiley series in computational statistics.
856	4	0	_uhttp://onlinelibrary.wiley.com/book/10.1002/9780470979174 _zWiley Online Library
942			_2ddc _cBK
999			_c205045 _d205045