Data Mining - Practical Machine Learning Tools and Techniques

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