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Book Description
practical machine learning tools and techniques
Table of Contents
- Front cover
- Data Mining: Practical Machine Learning Tools and Techniques
- Copyright page
- Table of contents
- List of Figures
- List of Tables
- Preface
- Updated and revised content
- Acknowledgments
- About the Authors
- PART I: Introduction to Data Mining
- Chapter 1: What’s It All About?
- Data mining and machine learning
- Simple examples: the weather and other problems
- Fielded applications
- Machine learning and statistics
- Generalization as search
- Data mining and ethics
- Further reading
- Chapter 2: Input: Concepts, Instances, and Attributes
- What’s a concept?
- What’s in an example?
- What’s in an attribute?
- Preparing the input
- Further reading
- Chapter 3: Output: Knowledge Representation
- Tables
- Linear models
- Trees
- Rules
- Instance-based representation
- Clusters
- Further reading
- Chapter 4: Algorithms: The Basic Methods
- InFerring rudimentary rules
- Statistical modeling
- Divide-and-conquer: constructing decision trees
- Covering algorithms: constructing rules
- Mining association rules
- Linear models
- Instance-based learning
- Clustering
- Multi-instance learning
- Further reading
- Weka implementations
- Chapter 5: Credibility: Evaluating What’s Been Learned
- Training and testing
- Predicting performance
- Cross-validation
- Other estimates
- Comparing data mining schemes
- Predicting probabilities
- Counting the cost
- Evaluating numeric prediction
- Minimum description length principle
- Applying the MDL principle to clustering
- Further reading
- Chapter 1: What’s It All About?
- Part 2: Advanced Data Mining
- Chapter 6: Implementations: Real Machine Learning Schemes
- Decision trees
- Classification rules
- Association rules
- Extending linear models
- Instance-based learning
- Numeric prediction with local linear models
- Bayesian networks
- Clustering
- Semisupervised learning
- Multi-instance learning
- Weka implementations
- Chapter 7: Data Transformations
- Attribute selection
- Discretizing numeric attributes
- Projections
- Sampling
- Cleansing
- Transforming multiple classes to binary ones
- Calibrating class probabilities
- Further reading
- Weka implementations
- Chapter 8: Ensemble Learning
- Combining multiple models
- Bagging
- Randomization
- Boosting
- Additive regression
- Interpretable ensembles
- Stacking
- Further reading
- Weka implementations
- Chapter 9: Moving on: Applications and Beyond
- Applying data mining
- Learning from massive datasets
- Data stream learning
- Incorporating domain knowledge
- Text mining
- Web mining
- Adversarial situations
- Ubiquitous data mining
- Further reading
- Chapter 6: Implementations: Real Machine Learning Schemes
- PART III: The Weka Data Mining Workbench
- Chapter 10: Introduction to Weka
- What’s in weka?
- How do you use it?
- What else can you do?
- How do you get it?
- Chapter 11: The Explorer
- Getting started
- Exploring the explorer
- Filtering algorithms
- Learning algorithms
- Metalearning algorithms
- Clustering algorithms
- Association-rule learners
- Attribute selection
- Chapter 12: The Knowledge Flow Interface
- Getting started
- Components
- Configuring and connecting the components
- Incremental learning
- Chapter 13: The Experimenter
- Getting started
- Simple setup
- Advanced setup
- The analyze panel
- Distributing processing over several machines
- Chapter 14: The Command-Line Interface
- Getting started
- The structure of weka
- Command-line options
- Chapter 15: Embedded Machine Learning
- A simple data mining application
- Chapter 16: Writing New Learning Schemes
- An example classifier
- Conventions for implementing classifiers
- Chapter 17: Tutorial Exercises for the Weka Explorer
- Introduction to the explorer interface
- Nearest-neighbor learning and decision trees
- Classification boundaries
- Preprocessing and parameter tuning
- Document classification
- Mining association rules
- Chapter 10: Introduction to Weka
- References
- Index
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