| Description |
1 online resource (xxiv, 404 pages) : illustrations |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
Introduction -- Overview of the data mining process -- Data visualization -- Dimension reduction -- Evaluating classification and predictive performance -- Multiple linear regression -- knearest neighbors (kNN) -- Naive bayes -- Classification and regression trees -- Logistic regression -- Neural nets -- Discriminant analysis -- Association rules -- Cluster Analysis -- Handling time series -- Regression-based forecasting -- Smoothing methods -- Cases. |
| Summary |
Data Mining for Business Intelligence, Second Edition uses real data and actual cases to illustrate the applicability of data mining (DM) intelligence in the development of successful business models. Featuring complimentary access to XLMiner, the Microsoft Office Excel add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of DM techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples, now doubled in number in the second edit. |
| Subject |
Microsoft Excel (Computer file)
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Microsoft Excel (Computer file) |
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Microsoft Excel (Computer file) |
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Business -- Data processing.
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Data mining.
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Management -- Data processing.
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Gestion -- Informatique. |
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Exploration de données (Informatique) |
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Business -- Data processing. |
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Data mining. |
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Management -- Data processing |
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Business -- Data processing |
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Data mining |
| Added Author |
Patel, Nitin R. (Nitin Ratilal)
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Bruce, Peter C., 1953-
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| Other Form: |
Print version: Shmueli, Galit, 1971- Data mining for business intelligence. 2nd ed. Hoboken, N.J. : Wiley, ©2010 9780470526828 (DLC) 2010005152 (OCoLC)531718699 |
| ISBN |
(cloth) |
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(cloth) |
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