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Data mining and statistics for decision making. Foreword by David J. Hand. Translated from the 3rd French ed. by Rod Riesco. (English) Zbl 1216.62005
Wiley Series in Computational Statistics. Hoboken, NJ: John Wiley & Sons (ISBN 978-0-470-68829-8/hbk; 978-0-470-97917-4/ebook). xxiv, 689 p. (2011).
Publisher’s description: Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is an ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization’s need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives.
This book looks at both classical and modern methods of data mining, such as clustering, discriminant analysis, decision trees, neural networks and support vector machines along with illustrative examples throughout the book to explain the theory of these models. Recent methods such as bagging and boosting, decision trees, neural networks, support vector machines and genetic algorithms are also discussed along with their advantages and disadvantages. Business intelligence analysts and statisticians, compliance and financial experts in both commercial and government organizations across all industry sectors will benefit from this book.

For the original French editions see [Zbl 1269.62019; Zbl 1270.62016].

62-07 Data analysis (statistics) (MSC2010)
68P10 Searching and sorting
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics
90B50 Management decision making, including multiple objectives
90B99 Operations research and management science
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