Applied Data Mining: Statistical Methods for Business and Industry
Author(s): Paolo Giudici (Author)
Publisher: John Wiley & Sons
Publication Date: 1 Oct. 2003
Edition: 1st
Language: English
Print length: 380 pages
ISBN-10: 0470846798
ISBN-13: 9780470846797
Book Description
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 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. Data sets used in the case studies are available at ftp://ftp.wiley.co.uk/pub/books/giudici
Editorial Reviews
Review
“…enlightening to anyone entering the area of data mining…a nice balance between theory and applications…certainly recommend it…” — Short Book Reviews, 2004
“…strength lies in the number and diversity of [these] case studies…extensive reference section…no hesitation in recommending this…” —
Significance – new magazine of the Royal Statistical Society, Vol 1(2), 2004
From the Back Cover
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.
Applied Data Mining: Statistical Methods for Business and Industry
Author(s): Paolo Giudici (Author)
Publisher: Wiley
Publication Date: October 17, 2003
Edition: 1st
Language: English
Print length: 376 pages
ISBN-10: 047084678X
ISBN-13: 9780470846780
Book Description
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
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.
Editorial Reviews
Review
“…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)
From the Back Cover
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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