Data mining and statistical methods used are the right tools to extract knowledge from data. 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. The second part of this book consists of nine case studies, drawn from the author of his own work in the industry, which shows how the methods described can be applied to real problems. Provides a solid introduction to data mining methods used in a consistent statistical framework Includes coverage of classical statistical methodology, multivariate and Bayesian 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 feature a number of studies detailed case based on projects implemented in industry discussions Combining the software used in data mining, with particular emphasis on SAS Supported by a website featuring data sets, software and additional materials including extensive bibliography and pointers to further reading in the Writers text has many years experience teaching introductory and multivariate statistics and data mining, and work on projects implemented in the 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.