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Data mining

Name: Data mining
ISBN: 978-0-12-374856-0

By I. H. Witten

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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
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
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
References
Index