Data mining and statistics for decision making / (Record no. 205045)
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| control field | OCoLC |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20171119091211.0 |
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| fixed length control field | 110428s2011 enka ob 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780470979174 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 0470979178 |
| Qualifying information | (electronic bk.) |
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| Qualifying information | (electronic bk.) |
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| Qualifying information | (electronic bk.) |
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| International Standard Book Number | 1283373971 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
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| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
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| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
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| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9780470688298 |
| Qualifying information | (hardback) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 0470688297 |
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| 037 ## - SOURCE OF ACQUISITION | |
| Stock number | 10.1002/9780470979174 |
| Source of stock number/acquisition | Wiley InterScience |
| Note | http://www3.interscience.wiley.com |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | UIU |
| Language of cataloging | eng |
| Description conventions | pn |
| Transcribing agency | UIU |
| Modifying agency | YDXCP |
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| 041 1# - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| Language code of original | fre |
| 049 ## - LOCAL HOLDINGS (OCLC) | |
| Holding library | MAIN |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA76.9.D343 |
| Item number | T84 2011 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM |
| Subject category code subdivision | 021030 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | PBT |
| Source | bicssc |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3/12 |
| Edition number | 22 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Tuffery, Stephane. |
| 245 10 - TITLE STATEMENT | |
| Title | Data mining and statistics for decision making / |
| Statement of responsibility, etc. | Stephane Tuffery. |
| Medium | [electronic resource] |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | Chichester, West Sussex ; |
| -- | Hoboken, NJ. : |
| Name of publisher, distributor, etc. | Wiley, |
| Date of publication, distribution, etc. | 2011. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (xxiv, 689 pages) : |
| Other physical details | illustrations. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS | |
| File type | data file |
| Source | rda |
| 380 ## - FORM OF WORK | |
| Form of work | Bibliography |
| 490 1# - SERIES STATEMENT | |
| Series statement | Wiley series in computational statistics |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Front 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# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Machine 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 ## - SUMMARY, ETC. | |
| Summary, etc. | "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"-- |
| Assigning source | Provided by publisher. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | "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"-- |
| Assigning source | Provided by publisher. |
| 588 0# - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Print version record. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistical decision. |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data mining. |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistical decision. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | COMPUTERS |
| General subdivision | Database Management |
| -- | Data Mining. |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data mining. |
| Source of heading or term | fast |
| Authority record control number | (OCoLC)fst00887946 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistical decision. |
| Source of heading or term | fast |
| Authority record control number | (OCoLC)fst01132059 |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| Main entry heading | Tuffery, Stephane. |
| Title | Data mining and statistics for decision making. |
| Place, publisher, and date of publication | Chichester, West Sussex ; Hoboken, NJ. : Wiley, 2011 |
| International Standard Book Number | 9780470688298 |
| Record control number | (DLC) 2010039789 |
| -- | (OCoLC)669160723 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Wiley series in computational statistics. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://onlinelibrary.wiley.com/book/10.1002/9780470979174 |
| Public note | Wiley Online Library |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Books |
No items available.