Applied Data Mining for Business and Industry, Second Edition

✍ Scribed by Paolo Giudici, Silvia Figini(auth.)

• Year: 2009
• Tongue: English
• Leaves: 251
• Category: Library

✦ Synopsis

The increasing availability of data in our current, information overloaded 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 knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications.

• Introduces data mining methods and applications.
• Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods.
• Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining.
• Features detailed case studies based on applied projects within industry.
• Incorporates discussion of data mining software, with case studies analysed using R.
• Is accessible to anyone with a basic knowledge of statistics or data analysis.
• Includes an extensive bibliography and pointers to further reading within the text.

Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance.Content:
Chapter 1 Introduction (pages 1–4):
Chapter 2 Organisation of the Data (pages 7–12):
Chapter 3 Summary Statistics (pages 13–40):
Chapter 4 Model Specification (pages 41–146):
Chapter 5 Model Evaluation (pages 147–162):
Chapter 6 Describing Website Visitors (pages 165–173):
Chapter 7 Market Basket Analysis (pages 175–191):
Chapter 8 Describing Customer Satisfaction (pages 193–202):
Chapter 9 Predicting Credit Risk of Small Businesses (pages 203–210):
Chapter 10 Predicting e?Learning Student Performance (pages 211–218):
Chapter 11 Predicting Customer Lifetime Value (pages 219–226):
Chapter 12 Operational Risk Management (pages 227–236):