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.
