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Detalles bibliogr�ficos

T�tulo

Applied Data Mining: Statistical Methods for Business and Industry (Statistics in Practice)

Editor

Wiley

Fecha de publicaci�n

2003

Idioma

Ingl�s

ISBN 10

0470846798

ISBN 13

9780470846797

Encuadernaci�n

Encuadernaci�n de tapa blanda

Estado

acceptable

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Sinopsis

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

De la contraportada

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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Descripci�n de la librer�a

Goodwill of Silicon Valley?s mission is to support our employees, our customers, and people with challenging barriers to employment; to raise their standard of living and improve their lives through our services and social enterprise. Founded in Santa Clara County in 1928, Goodwill of Silicon Valley is dedicated to improving employment opportunities, increasing standards of living, providing economic independence, and restoring our clients? sense of self-worth. We do this through workforce creation, vocational training, and environmental stewardship. With 18 retail stores, an online store, an extensive reuse/recycling operation, and our Contract Services division, we help people overcome barriers to employment, build sustainable livelihoods, and transform their lives and communities.

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Goodwill of Silicon Valley
1080 North 7th Street, San Jose, CA, 95112, U.S.A.

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