Data mining : practical machine learning tools and techniques /
Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal.
- Fourth Edition.
- xxxii, 621 pages ; 24 cm
Rev. edition of: Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. c2013.
Includes bibliographical references (pages 573-601) and index.
Table of Contents
Part I: Introduction to data mining
Chapter 1. What?s it all about?
Abstract 1.1 Data Mining and Machine Learning 1.2 Simple Examples: The Weather Problem and Others 1.3 Fielded Applications 1.4 The Data Mining Process 1.5 Machine Learning and Statistics 1.6 Generalization as Search 1.7 Data Mining and Ethics 1.8 Further Reading and Bibliographic Notes
Chapter 2. Input: Concepts, instances, attributes
Abstract 2.1 What?s a Concept? 2.2 What?s in an Example? 2.3 What?s in an Attribute? 2.4 Preparing the Input 2.5 Further Reading and Bibliographic Notes
Chapter 3. Output: Knowledge representation
Abstract 3.1 Tables 3.2 Linear Models 3.3 Trees 3.4 Rules 3.5 Instance-Based Representation 3.6 Clusters 3.7 Further Reading and Bibliographic Notes
Chapter 4. Algorithms: The basic methods
Abstracts 4.1 Inferring Rudimentary Rules 4.2 Simple Probabilistic Modeling 4.3 Divide-and-Conquer: Constructing Decision Trees 4.4 Covering Algorithms: Constructing Rules 4.5 Mining Association Rules 4.6 Linear Models 4.7 Instance-Based Learning 4.8 Clustering 4.9 Multi-instance Learning 4.10 Further Reading and Bibliographic Notes 4.11 Weka Implementations
Chapter 5. Credibility: Evaluating what?s been learned
Abstract 5.1 Training and Testing 5.2 Predicting Performance 5.3 Cross-Validation 5.4 Other Estimates 5.5 Hyperparameter Selection 5.6 Comparing Data Mining Schemes 5.7 Predicting Probabilities 5.8 Counting the Cost 5.9 Evaluating Numeric Prediction 5.10 The MDL Principle 5.11 Applying the MDL Principle to Clustering 5.12 Using a Validation Set for Model Selection 5.13 Further Reading and Bibliographic Notes
Part II: More advanced machine learning schemes
Chapter 6. Trees and rules
Abstract 6.1 Decision Trees 6.2 Classification Rules 6.3 Association Rules 6.4 Weka Implementations
Chapter 7. Extending instance-based and linear models
Abstract 7.1 Instance-Based Learning 7.2 Extending Linear Models 7.3 Numeric Prediction With Local Linear Models 7.4 Weka Implementations
Chapter 8. Data transformations
Abstracts 8.1 Attribute Selection 8.2 Discretizing Numeric Attributes 8.3 Projections 8.4 Sampling 8.5 Cleansing 8.6 Transforming Multiple Classes to Binary Ones 8.7 Calibrating Class Probabilities 8.8 Further Reading and Bibliographic Notes 8.9 Weka Implementations
Chapter 9. Probabilistic methods
Abstract 9.1 Foundations 9.2 Bayesian Networks 9.3 Clustering and Probability Density Estimation 9.4 Hidden Variable Models 9.5 Bayesian Estimation and Prediction 9.6 Graphical Models and Factor Graphs 9.7 Conditional Probability Models 9.8 Sequential and Temporal Models 9.9 Further Reading and Bibliographic Notes 9.10 Weka Implementations
Chapter 10. Deep learning
Abstract 10.1 Deep Feedforward Networks 10.2 Training and Evaluating Deep Networks 10.3 Convolutional Neural Networks 10.4 Autoencoders 10.5 Stochastic Deep Networks 10.6 Recurrent Neural Networks 10.7 Further Reading and Bibliographic Notes 10.8 Deep Learning Software and Network Implementations 10.9 WEKA Implementations
Chapter 11. Beyond supervised and unsupervised learning
Abstract 11.1 Semisupervised Learning 11.2 Multi-instance Learning 11.3 Further Reading and Bibliographic Notes 11.4 WEKA Implementations
Abstract 13.1 Applying Machine Learning 13.2 Learning From Massive Datasets 13.3 Data Stream Learning 13.4 Incorporating Domain Knowledge 13.5 Text Mining 13.6 Web Mining 13.7 Images and Speech 13.8 Adversarial Situations 13.9 Ubiquitous Data Mining 13.10 Further Reading and Bibliographic Notes 13.11 WEKA Implementations
Appendix A. Theoretical foundations
A.1 Matrix Algebra A.2 Fundamental Elements of Probabilistic Methods
Appendix B. The WEKA workbench
B.1 What?s in WEKA? B.2 The package management system B.3 The Explorer B.4 The Knowledge Flow Interface B.5 The Experimenter
Description
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. View more > Key Features
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book