Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal.

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

Chapter 12. Ensemble learning

Abstract
12.1 Combining Multiple Models
12.2 Bagging
12.3 Randomization
12.4 Boosting
12.5 Additive Regression
12.6 Interpretable Ensembles
12.7 Stacking
12.8 Further Reading and Bibliographic Notes
12.9 WEKA Implementations

Chapter 13. Moving on: applications and beyond

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