The increasing availability of data in our current, informationoverloaded society has led to the need for valid tools for itsmodelling and analysis. Data mining and applied statistical methodsare the appropriate tools to extract knowledge from such data. Thisbook provides an accessible introduction to data mining methods ina consistent and application oriented statistical framework, usingcase studies drawn from real industry projects and highlighting theuse of data mining methods in a variety of business applications.

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

Applied Data Mining for Business and Industry, 2ndedition is aimed at advanced undergraduate and graduatestudents of data mining, applied statistics, database management,computer science and economics. The case studies will provideguidance to professionals working in industry on projects involvinglarge volumes of data, such as customer relationship management,web design, risk management, marketing, economics and finance.