Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall.

Contents

Part I Introduction ti data mining

Chapter 1 What its all about?
1.1 Data mining and machine learning
1.2 Simple examples: the weather problem and others
1.3 Fielded applications
1.4 Machine learning and ststistics
1.5 Generalization as search
Etc.

Chapter 2 Input concepts, Instances and Attributes
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
Etc.

Chapter 3:Output: Knowledge Representation
3.1 Tables
3.2 Linear Models
3.3 Trees
3.4 Rules
3.5 Instance-Based representation
Etc.

Chapter 4: Algorithms: The basic Metods
4.1 Inferring rudimentary rules
4.2 Statistical modeling
4.3 Divide-and-conquer: Constructing decision tress
4.4 Covering algorithms: Constructing rules
4.5 Mining association rules
Etc.

Chapter 5: Credibility: Evaluating what's been learned
5.1 Training and testing
5.2 Predicting performance
5.3 Cross-validation
5.4 Other estimates
5.5 Comparing data mining schemes
Etc.

Part II Advanced Data Mining

Chapter 6: Implementations: Real machine learning schemes
6.1 Decision trees
6.2 Classification rules
6.3 Association rules
6.4 Extending linear models
6.5 Instance-based learning
Etc.

Chapter 7: Dta Transformations
7.1 Attribute selection
7.2 Discretizing numeric attributes
7.3 Projects
7.4 Sampling
7.5 Cleaning
Etc.

Chapter 8: Ensemble Learing
8.1 Combining multiple models
8.2 Bagging
8.3 Randomization
8.4 Boosting
8.5 Additive logistic regression
Etc.

Chapter 9: Moving on: Applications and beyond
9.1 Applying datamining
9.2 Learning from massive datasets
9.3 Data stream learning
9.4 Incorporating domain Knowledge
9.5 Text mining
Etc.

Part III The Weka Data Mining Workbench

Chapter 10: Introduction to Weka
10.1 What's in Weka?
10.2 How do you use it?
10.3 What else can you Do?
10.4 How doyou get it?

Chapter 11: The Explorer
11.1 Getting started
11.2 Exploring the Explorer
11.3 Filtering Algorithms
11.4 Learning Algorithms
11.5 Metalearning Algorithms
Etc.

Chapter 12: The Knowledge flow Interface
12.1 Getting Started
12.2 Components
12.3 Configuring and Constructing the Components
12.4 Incremental Learning

Chapter 13: The Experimenter
13.1 Getting Started
13.2 Simple setup
13.3 Advanced setup
13.4 The Analyze Panel
13.5 Distributing processing over several Machines

Chapter 14: The Command-Line Interface
14.1 Getting Started
14.2 The structure of Weka
14.3 Command-Line Options

Chapter 15: Embedded Machine Learning
15.1 A Simple Data Mining Application

Chapter 16: Writing new learning Schemes
16.1 An example Classifier
16.2 Conventions for Implementing Classifiers

Chapter 17: Tutorial Exercises for the Weka Explorer
17.1 Introduction to the Explorer Interface
17.2 Nearest Neighbor Learning and Decision Trees
17.3 Classification Boundaries
17.4 Preprocessing and parameter tuning
17.5 Document Classification

Includes bibliographical references (p. 587-605) and index. 629-609 p.