| Description |
xxxiii, 629 pages : illustrations ; 24 cm |
| Series |
The Morgan Kaufmann series in data management systems |
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Morgan Kaufmann series in data management systems.
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| Contents |
PART I: Machine Learning Tools and Techniques. Ch 1. What's It All About? Ch 2. Input: Concepts, Instances, Attributes. Ch 3. Output: Knowledge Representation. Ch 4. Algorithms: The Basic Methods. Ch 5. Credibility: Evaluating What's Been Learned. PART II: Advanced Data Mining.Ch 6. Implementations: Real Machine Learning Schemes. Ch 7. Data Transformation. Ch 8. Ensemble Learning. Ch 9. Moving On: Applications and Beyond. PART III: The Weka Data MiningWorkbench. Ch 10. Introduction to Weka. Ch 11. The Explorer. Ch 12. The Knowledge Flow Interface. Ch 13. The Experimenter. Ch 14 The Command-Line Interface. Ch 15. Embedded Machine Learning. Ch 16. Writing New Learning Schemes. Ch 17. Tutorial Exercises for the Weka Explorer |
| Summary |
-Algorithms in the toolkit cover: data preprocessing, classification, regression, clustering, association rules, and visualization --Book Jacket |
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-Includes downloadable Weka software toolkit-a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface -- |
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-Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods -- |
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Key Features -- |
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This third edition of Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This, highly anticipated revision of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. -- |
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Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Datasets, Multi-Instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. -- |
| Notes |
Includes index |
| Bibliography |
Includes bibliographical references and index |
| Notes |
eBook access via Internet and the Adobe Reader (for *.pdf files) |
|
Print version record |
| Subject |
Data mining.
|
|
Data Mining.
|
| Author |
Hall, Mark A., author
|
| LC no. |
2010039827 |
| ISBN |
9780123748560 (paperback) |
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