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Applied Data Mining: Statistical Methods for Business and Industry

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"…a book with many nice features that has elements of interest for …every subset of the intended audience…"
 (Journal of the American Statistical Association, September 2006)

"The author's style is consistently readable. Stripping out all but the barest essential mathematics makes the remaining material very approachable to a model-centric audience."
(Technometrics, February 2005)

Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information 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 such knowledge from data. Applications occur in many different fields, including statistics, computer science, machine learning, economics, marketing and finance.

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

The increasing availability of data in the current information 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. Applied Data Mining: Statistical Methods for Business and Industry provides an accessible introduction to data mining methods in a consistent and application-oriented statistical framework. It describes six case studies, taken from real industry projects, highlighting the current applications of data mining methods.

  • Provides an introduction to data mining methods and applications.
  • Includes coverage of classical and Bayesian multivariate statistical methodology as well as of machine learning and computational data mining methods.
  • Includes many recent developments, such as association and sequence rules, graphical Markov models, memory-based reasoning, credit risk and web mining.
  • Features a number of detailed case studies based on applied projects within industry.
  • Incorporates discussion of data mining software, and the case studies are analysed using SAS and SAS Enterprise Miner.
  • 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: Statistical Methods for Business and Industry is primarily aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies give guidance to professionals working in industry on projects involving large volumes of data, such as in customer relationship management, web design, risk management, marketing, economics and finance.

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