Data Mining and Statistics for Decision Making
Data Mining and Statistics for Decision Making
This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations.
Key Features:
- Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques.
- Starts from basic principles up to advanced concepts.
- Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software.
- Gives practical tips for data mining implementation to solve real world problems.
- Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring.
- Supported by an accompanying website hosting datasets and user analysis.
Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.
Author: Tuffã(c)Ry
Publisher: Wiley
Published: 04/18/2011
Pages: 720
Binding Type: Hardcover
Weight: 2.95lbs
Size: 9.80h x 6.90w x 1.70d
ISBN: 9780470688298
Review Citation(s):
Reference and Research Bk News 06/01/2011 pg. 206
About the Author
Dr Stephane Tuffery teaches Data Mining and statistics, University Rennes 1, Paris, France.
Translator, Rod Riesco, UK.
This title is not returnable