Format:
Online-Ressource (xxxiii, 629 p)
,
ill
Edition:
3. ed.
Edition:
Online-Ausg. 2012 Electronic reproduction; Available via World Wide Web
ISBN:
0080890369
,
9780080890364
Series Statement:
[Morgan Kaufmann series in data management systems]
Content:
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 third edition 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. 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 Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research
Content:
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 third edition 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. 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 Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
Note:
Machine generated contents note: 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
,
Includes bibliographical references and index
,
Front cover; Data Mining: Practical Machine Learning Tools and Techniques; Copyright page; Table of Contents; List of Figures; List of Tables; Preface; Updated and revised content; Second Edition; Third Edition; Acknowledgments; About the Authors; Part I Introduction to Data Mining; CHAPTER 1 What's It All About?; 1.1 Data mining and machine learning; Describing Structural Patterns; Machine Learning; Data Mining; 1.2 Simple examples: the weather and other problems; The Weather Problem; Contact Lenses: An Idealized Problem; Irises: A Classic Numeric Dataset
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CPU Performance: Introducing Numeric PredictionLabor Negotiations: A More Realistic Example; Soybean Classification: A Classic Machine Learning Success; 1.3 Fielded applications; Web Mining; Decisions Involving Judgment; Screening Images; Load Forecasting; Diagnosis; Marketing and Sales; Other Applications; 1.4 Machine learning and statistics; 1.5 Generalization as search; 1.6 Data mining and ethics; Reidentification; Using Personal Information; Wider Issues; 1.7 Further reading; CHAPTER 2 Input:; 2.1 What's a concept?; 2.2 What's in an example?; Relations; Other Example Types
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2.3 What's in an attribute?2.4 Preparing the input; Gathering the Data Together; ARFF Format; Sparse Data; Attribute Types; Missing Values; Inaccurate Values; Getting to Know Your Data; 2.5 Further reading; CHAPTER 3 Output:; 3.1 Tables; 3.2 Linear models; 3.3 Trees; 3.4 Rules; Classification Rules; Association Rules; Rules with Exceptions; More Expressive Rules; 3.5 Instance-based representation; 3.6 Clusters; 3.7 Further reading; CHAPTER 4 Algorithms:; 4.1 InFerring rudimentary rules; Missing Values and Numeric Attributes; Discussion; 4.2 Statistical modeling
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Missing Values and Numeric AttributesNaïve Bayes for Document Classification; Discussion; 4.3 Divide-and-conquer: constructing decision trees; Calculating Information; Highly Branching Attributes; Discussion; 4.4 Covering algorithms: constructing rules; Rules versus Trees; A Simple Covering Algorithm; Rules versus Decision Lists; 4.5 Mining association rules; Item Sets; Association Rules; Generating Rules Efficiently; Discussion; 4.6 Linear models; Numeric Prediction: Linear Regression; Linear Classification: Logistic Regression; Linear Classification Using the Perceptron
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Linear Classification Using Winnow4.7 Instance-based learning; Distance Function; Finding Nearest Neighbors Efficiently; Discussion; 4.8 Clustering; Iterative Distance-Based Clustering; Faster Distance Calculations; Discussion; 4.9 Multi-instance learning; Aggregating the Input; Aggregating the Output; Discussion; 4.10 Further reading; 4.11 Weka implementations; CHAPTER 5 Credibility:; 5.1 Training and testing; 5.2 Predicting performance; 5.3 Cross-validation; 5.4 Other estimates; Leave-One-Out Cross-Validation; The Bootstrap; 5.5 Comparing data mining schemes; 5.6 Predicting probabilities
,
Quadratic Loss Function
,
Electronic reproduction; Available via World Wide Web
Additional Edition:
ISBN 9780123748560
Additional Edition:
Print version Data Mining Practical Machine Learning Tools and Techniques
Additional Edition:
Druckausg. Witten, Ian H., 1947 - Data mining Amsterdam : Morgan Kaufmann, 2011 ISBN 9780123748560
Language:
English
Subjects:
Computer Science
,
Economics
,
Psychology
Keywords:
Data Mining
URL:
Volltext
(An electronic book accessible through the World Wide Web; click for information)
Author information:
Witten, Ian H. 1947-
Author information:
Frank, Eibe
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