Data Mining Practical Machine Learning Tools and Techniques, 4/Ed > 데이터마이닝

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