Data Mining and Statistics for Decision Making (Hardcover)

Stephane Tuffery

ISBN 10: 0470688297 / ISBN 13: 9780470688298

Editore: John Wiley & Sons Inc, New York, 2011

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Hardcover. 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 the 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 recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques.Starts from basic principles up to advanced concepts.Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software.Gives practical tips for data mining implementation to solve real world problems.Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring.Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book. 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 the ideal tool for such an extraction of knowledge. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Codice articolo 9780470688298

Dati bibliografici

• Titolo: Data Mining and Statistics for Decision Making (Hardcover)
• Editore: John Wiley & Sons Inc, New York
• Data di pubblicazione: 2011
• Lingua: Inglese
• ISBN 10: 0470688297
• ISBN 13: 9780470688298
• Rilegatura: Hardcover
• Condizione: new

Descrizione articolo

Riassunto

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 the 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 recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations.

 Key Features:

• Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques.
• Starts from basic principles up to advanced concepts.
• Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software.
• Gives practical tips for data mining implementation to solve real world problems.
• Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring.
• Supported by an accompanying website hosting datasets and user analysis. 

Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.

Informazioni sull?autore

Dr Stephane Tuffery teaches Data Mining and statistics, University Rennes 1, Paris, France.

Translator, Rod Riesco, UK.