
Applied Data Mining
Statistical Methods for Business and Industry
Paolo Giudici(Author)
Wiley (Publisher)
1st Edition
Published on 22. August 2003
Book
Hardback
376 pages
978-0-470-84678-0 (ISBN)
Article exhausted; check for reprint
Description
Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract such knowledge from data. Applications occur in many different fields, including statistics, computer science, machine learning, economics, marketing and finance. This book is the first to describe applied data mining methods in a consistent statistical framework, and then show how they can be applied in practice. There is a real demand for such a book, as most of these available on the market are either too technical and computer science oriented or too applied and marketing driven.
Reviews / Votes
" a book with many nice features that has elements of interest for every subset of the intended audience " (Journal of the American Statistical Association, September 2006) "The author s style is consistently readable. Stripping out all but the barest essential mathematics makes the remaining material very approachable to a model centric audience." (Technometrics, February 2005)More details
Edition
1., Auflage
Language
English
Place of publication
Chichester
United Kingdom
Publishing group
John Wiley and Sons Ltd
Target group
College/higher education
Professional and scholarly
Edition type
New edition
Illustrations
bibliography
Dimensions
Height: 23.8 cm
Width: 16.3 cm
Thickness: 27 mm
Weight
664 gr
ISBN-13
978-0-470-84678-0 (9780470846780)
Schweitzer Classification
Other editions
New editions

Paolo Giudici | Silvia Figini
Applied Data Mining for Business and Industry
Book
04/2009
2nd Edition
Wiley
€159.50
Shipment within 10-20 days
Content
Preface.
1. Introduction.
PART I: METHODOLOGY.
2. Organisation of the data.
3. Exploratory data analysis.
4. Computational data mining.
5. Statistical data mining.
6. Evaluation of data mining methods.
PART II: BUSINESS CASES.
7. Market basket analysis.
8. Web clickstream analysis.
9. Profiling website visitors.
10. Customer relationship management.
11. Credit scoring.
12. Forecasting television audience.
Bibliography.
Index.
1. Introduction.
PART I: METHODOLOGY.
2. Organisation of the data.
3. Exploratory data analysis.
4. Computational data mining.
5. Statistical data mining.
6. Evaluation of data mining methods.
PART II: BUSINESS CASES.
7. Market basket analysis.
8. Web clickstream analysis.
9. Profiling website visitors.
10. Customer relationship management.
11. Credit scoring.
12. Forecasting television audience.
Bibliography.
Index.