Data Mining by Witten, Ian H. -- Read -- Imperial Library of Trantor

Index

Cover image Title page Table of Contents Copyright List of Figures List of Tables Preface

Updated and Revised Content Acknowledgments

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

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

References Index