Data mining and statistics for decision making

"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"-- Provided by publisher

Bibliographic Details
Main Author: Tuffery, Stephane (Author)
Format: Book
Language:English
Published: Chichester, West Sussex ; Hoboken, New Jersey : Wiley , c2011
Series:Wiley series in computational statistics
Subjects:
Online Access:Cover image

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005 20130225093000.0
008 120628s2011 enk eng
020 |a 0470688297 (hardback : alk. paper) 
020 |a 9780470688298 (hardback : alk. paper) 
050 0 0 |a QA76.9.D343   |b T84 2011 
090 0 0 |a QA76.9.D343   |b T84 2011 
100 1 |a Tuffery, Stephane ,   |e author 
245 1 0 |a Data mining and statistics for decision making   |c Stephane Tuffery 
260 |a Chichester, West Sussex ;   |a Hoboken, New Jersey :   |b Wiley ,   |c c2011 
300 |a xxiv, 689 p. :   |b ill. ;   |c 25 cm. 
490 1 |a Wiley series in computational statistics 
504 |a Includes bibliographical references and index 
505 0 |a 1. Overview of data mining -- 2. The development of a data mining study -- 3. Data exploration and preparation -- 4. Using commercial data -- 5. Statistical and data mining software -- 6. An outline of data mining methods -- 7. Factor analysis -- 8. Neural networks -- 9. Cluster analysis -- 10. Association analysis -- 11. Classification and prediction methods -- 12. An application of data mining: scoring -- 13. Factors for success in a data mining project -- 14. Text mining 
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"-- Provided by publisher 
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"-- Provided by publisher 
650 0 |a Data mining 
650 0 |a Statistical decision 
856 4 2 |3 Cover image   |u http://catalogimages.wiley.com/images/db/jimages/9780470688298.jpg 
999 |a 1000152651   |b Book   |c OPEN SHELF (30 DAYS)   |e Tembila Campus