Data Mining Statistics Decision by Tufféry Stéphane (16 results)

- Hardcover
Seller: Zubal-Books, Since 1961, Cleveland, OH, U.S.A.Zubal-Books, Since 1961
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Condition: Very Good. *Price HAS BEEN REDUCED by 10% until Monday, Aug. 10 (weekend SALE item)* 720 pp., hardcover, very good. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees requ…ired by recipient's country.

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- Hardcover
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Condition: New. 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. Series: Wiley Series in Computational Statistics. Num Pages: 716 pa…ges, Illustrations, charts, tables. BIC Classification: PBT; UNF. Category: (P) Professional & Vocational. Dimension: 250 x 172 x 53. Weight in Grams: 1340. . 2011. 2nd Edition. Hardcover. . . . . Books ship from the US and Ireland.

- Hardcover
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Hardcover. Condition: Brand New. 1st edition. 716 pages. 9.84x6.85x1.73 inches. In Stock.

- Hardcover
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Buch. Condition: Neu. Neuware - 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 o…r 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.

- Hardcover
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- Hardcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Hardcover. Condition: new. 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 busi…ness 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

- Hardcover
- Print on Demand
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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£ 98.15
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Hardcover. Condition: Brand New. 1st edition. 716 pages. 9.84x6.85x1.73 inches. In Stock. This item is printed on demand.