Data mining
Free
Description
Contents
Reviews
Language
English
ISBN
978-0-12-374856-0
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
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