Data Mining and Statistics for Decision Making

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9780470688298: Data Mining and Statistics for Decision Making

Data mining is the process of automatically searching large volumesof data for models and patterns using computational techniques fromstatistics, machine learning and information theory; it is theideal tool for such an extraction of knowledge. Data mining isusually associated with a business or an organization's need toidentify trends and profiles, allowing, for example, retailers todiscover patterns on which to base marketing objectives.

This book looks at both classical and recent techniques of datamining, such as clustering, discriminant analysis, logisticregression, generalized linear models, regularized regression, PLSregression, decision trees, neural networks, support vectormachines, Vapnik theory, naive Bayesian classifier, ensemblelearning and detection of association rules. They are discussedalong with illustrative examples throughout the book to explain thetheory of these methods, as well as their strengths andlimitations.

Key Features: * Presents a comprehensive introduction to all techniques usedin data mining and statistical learning, from classical to latesttechniques. * 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 ofthose software. * Gives practical tips for data mining implementation to solvereal world problems. * Looks at a range of tools and applications, such asassociation rules, web mining and text mining, with a special focuson credit scoring. * Supported by an accompanying website hosting datasets and useranalysis.

Statisticians and business intelligence analysts, students aswell as computer science, biology, marketing and financial riskprofessionals in both commercial and government organizationsacross all business and industry sectors will benefit from thisbook.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

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Tuffery, Stephane
Editore: Wileyand#8211;Blackwell (2011)
ISBN 10: 0470688297 ISBN 13: 9780470688298
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Descrizione libro Wileyand#8211;Blackwell, 2011. HRD. Condizione libro: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Codice libro della libreria FW-9780470688298

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Descrizione libro John Wiley and Sons Ltd, United States, 2011. Hardback. Condizione libro: New. 2nd ed.. 250 x 174 mm. Language: English . Brand New 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. 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. Codice libro della libreria AAH9780470688298

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Descrizione libro Wiley, 2011. Condizione libro: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: This practical guide to understanding and implementing data mining techniques discusses traditional methods-cluster analysis, factor analysis, linear regression, PLS regression, and generalized linear models-and recent methods-bagging and boosting, decision trees, neural networks, support vector machines, and genetic algorithm. Codice libro della libreria ABE_book_new_0470688297

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Descrizione libro Wileyand#8211;Blackwell, 2011. HRD. Condizione libro: New. New Book.Shipped from US within 10 to 14 business days.THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice libro della libreria IP-9780470688298

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Descrizione libro John Wiley and Sons Ltd, United States, 2011. Hardback. Condizione libro: New. 2nd ed.. 250 x 174 mm. Language: English . Brand New 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. 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. Codice libro della libreria AAH9780470688298

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Descrizione libro John Wiley & Sons, 2016. Paperback. Condizione libro: New. PRINT ON DEMAND Book; New; Publication Year 2016; Not Signed; Fast Shipping from the UK. No. book. Codice libro della libreria ria9780470688298_lsuk

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Descrizione libro 2011. Hardback. Condizione libro: NEW. 9780470688298 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. Codice libro della libreria HTANDREE0778374

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Descrizione libro Wiley-Blackwell 2011-03-11, 2011. Condizione libro: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely. Codice libro della libreria NU-ING-00755928

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Descrizione libro Wiley, 2011. Hardcover. Condizione libro: New. book. Codice libro della libreria 0470688297

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