book

Data Mining, 4th Edition

Name: Data Mining, 4th Edition
ISBN: 9780128043578

by Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal

October 2016

Intermediate to advanced

654 pages

22h 41m

English

Morgan Kaufmann

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Cover image
Title page
Table of Contents
Copyright
List of Figures
List of Tables
Preface
Updated and Revised ContentAcknowledgments
Part I: Introduction to data mining
Chapter 1. What’s it all about?
Abstract1.1 Data Mining and Machine Learning1.2 Simple Examples: The Weather Problem and Others1.3 Fielded Applications1.4 The Data Mining Process1.5 Machine Learning and Statistics1.6 Generalization as Search1.7 Data Mining and Ethics1.8 Further Reading and Bibliographic Notes
Chapter 2. Input: Concepts, instances, attributes
Abstract2.1 What’s a Concept?2.2 What’s in an Example?2.3 What’s in an Attribute?2.4 Preparing the Input2.5 Further Reading and Bibliographic Notes

Chapter 3. Output: Knowledge representation
Abstract3.1 Tables3.2 Linear Models3.3 Trees3.4 Rules3.5 Instance-Based Representation3.6 Clusters3.7 Further Reading and Bibliographic Notes
Chapter 4. Algorithms: The basic methods
Abstracts4.1 Inferring Rudimentary Rules4.2 Simple Probabilistic Modeling4.3 Divide-and-Conquer: Constructing Decision Trees
4.4 Covering Algorithms: Constructing Rules
4.5 Mining Association Rules4.6 Linear Models4.7 Instance-Based Learning4.8 Clustering4.9 Multi-instance Learning4.10 Further Reading and Bibliographic Notes4.11 Weka Implementations
Chapter 5. Credibility: Evaluating what’s been learned
Abstract5.1 Training and Testing5.2 Predicting Performance5.3 Cross-Validation5.4 Other Estimates5.5 Hyperparameter Selection5.6 Comparing Data Mining Schemes5.7 Predicting Probabilities5.8 Counting the Cost5.9 Evaluating Numeric Prediction5.10 The MDL Principle5.11 Applying the MDL Principle to Clustering5.12 Using a Validation Set for Model Selection5.13 Further Reading and Bibliographic Notes
Part II: More advanced machine learning schemes
Part II. More advanced machine learning schemes
Chapter 6. Trees and rules
Abstract6.1 Decision Trees6.2 Classification Rules6.3 Association Rules6.4 Weka Implementations
Chapter 7. Extending instance-based and linear models
Abstract7.1 Instance-Based Learning7.2 Extending Linear Models7.3 Numeric Prediction With Local Linear Models7.4 Weka Implementations
Chapter 8. Data transformations
Abstracts8.1 Attribute Selection8.2 Discretizing Numeric Attributes8.3 Projections8.4 Sampling8.5 Cleansing8.6 Transforming Multiple Classes to Binary Ones8.7 Calibrating Class Probabilities8.8 Further Reading and Bibliographic Notes8.9 Weka Implementations
Chapter 9. Probabilistic methods
Abstract9.1 Foundations9.2 Bayesian Networks9.3 Clustering and Probability Density Estimation9.4 Hidden Variable Models9.5 Bayesian Estimation and Prediction9.6 Graphical Models and Factor Graphs
9.7 Conditional Probability Models
9.8 Sequential and Temporal Models
9.9 Further Reading and Bibliographic Notes9.10 Weka Implementations
Chapter 10. Deep learning
Abstract10.1 Deep Feedforward Networks10.2 Training and Evaluating Deep Networks10.3 Convolutional Neural Networks
10.4 Autoencoders
10.5 Stochastic Deep Networks10.6 Recurrent Neural Networks10.7 Further Reading and Bibliographic Notes10.8 Deep Learning Software and Network Implementations10.9 WEKA Implementations
Chapter 11. Beyond supervised and unsupervised learning
Abstract11.1 Semisupervised Learning11.2 Multi-instance Learning11.3 Further Reading and Bibliographic Notes11.4 WEKA Implementations
Chapter 12. Ensemble learning
Abstract12.1 Combining Multiple Models12.2 Bagging12.3 Randomization12.4 Boosting12.5 Additive Regression12.6 Interpretable Ensembles12.7 Stacking12.8 Further Reading and Bibliographic Notes12.9 WEKA Implementations
Chapter 13. Moving on: applications and beyond
Abstract13.1 Applying Machine Learning13.2 Learning From Massive Datasets13.3 Data Stream Learning13.4 Incorporating Domain Knowledge13.5 Text Mining13.6 Web Mining13.7 Images and Speech13.8 Adversarial Situations13.9 Ubiquitous Data Mining13.10 Further Reading and Bibliographic Notes13.11 WEKA Implementations
Appendix A. Theoretical foundations
A.1 Matrix AlgebraA.2 Fundamental Elements of Probabilistic Methods
Appendix B. The WEKA workbench
B.1 What’s in WEKA?B.2 The package management systemB.3 The ExplorerB.4 The Knowledge Flow InterfaceB.5 The Experimenter
References
Index

Content preview from Data Mining, 4th Edition

This is ordinary linear regression!

Although x_i is a scalar here, the method generalizes to a vector x_i. Categorical variables can be encoded into a subset of the dimensions of x using the so-called “one-hot” method, placing a 1 in the dimension corresponding to the category label and 0s in all other dimensions allocated to the variable. If all input variables are categorical, this corresponds to the classic analysis of variance (ANOVA) method.

Using Priors on Parameters

Placing a Gaussian prior on the parameters w leads to the method of ridge regression (Section 7.2)— also called “weight decay.” Consider a regression that uses a D-dimensional ...

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