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Applied Data Mining: Statistical Methods for Business and Industry
BY Paolo Giudici
MATHEMATICS & STATISTICS / DATA MINING STATISTICS /
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. All the methods described are either computational, or of a statistical modelling nature. Complex probabilistic models and mathematical tools are not used, so the book is accessible to a wide audience of students and industry professionals.
The second half of the book consists of nine case studies, taken from the author's own work in industry, that demonstrate how the methods described can be applied to real problems.
Provides a solid introduction to applied data mining methods in a consistent statistical framework
Includes coverage of classical, multivariate and Bayesian statistical methodology
Includes many recent developments such as web mining, sequential Bayesian analysis and memory based reasoning
Each statistical method described is illustrated with real life applications
Features a number of detailed case studies based on applied projects within industry
Incorporates discussion on software used in data mining, with particular emphasis on SAS
Supported by a website featuring data sets, software and additional material
Includes an extensive bibliography and pointers to further reading within the text
Author has many years experience teaching introductory and multivariate statistics and data mining, and working on applied projects within industry
A valuable resource for advanced undergraduate and graduate students of applied statistics, data mining, computer science and economics, as well as for professionals working in industry on projects involving large volumes of data - such as in marketing or financial risk management.
TABLE OF CONTENTS:
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.