- 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.