Make us homepage
Add to Favorites
FAIL (the browser should render some flash content, not this).

Main page » Fiction literature » Data Mining: Practical Machine Learning Tools and Techniques (Second Edition)

Data Mining: Practical Machine Learning Tools and Techniques (Second Edition)


Free software:
A comprehensive software resource, written in the Java language, has been created to illustrate the ideas in the book. Called the Waikato Environment for Knowledge Analysis, or Weka for short, it is available here

Features of the book
    * Explains how data mining algorithms work.
    * Helps you select appropriate approaches to particular problems and to compare and evaluate the results  of different techniques.
    * Covers performance improvement techniques, including input preprocessing and combining output from different methods.
    * Shows you how to use the Weka machine learning workbench.

Table of Contents for the 2nd Edition:

Part I: Practical Machine Learning Tools and Techniques

1. What’s it 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 statistics
1.5 Generalization as search
1.6 Data mining and ethics
1.7 Further reading

2. Input: Concepts, instances, 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

3. Output: Knowledge representation
3.1 Decision tables
3.2 Decision trees
3.3 Classification rules
3.4 Association rules
3.5 Rules with exceptions
3.6 Rules involving relations
3.7 Trees for numeric prediction
3.8 Instance-based representation
3.9 Clusters
3.10 Further reading

4. Algorithms: The basic methods
4.1 Inferring rudimentary rules
4.2 Statistical modeling
4.3 Divide-and-conquer: constructing decision trees
4.4 Covering algorithms: constructing rules
4.5 Mining association rules
4.6 Linear models
4.7 Instance-based learning
4.8 Clustering
4.9 Further reading

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
5.6 Predicting probabilities
5.7 Counting the cost
5.8 Evaluating numeric prediction
5.9 The minimum description length principle
5.10 Applying MDL to clustering
5.11 Further reading

6. Implementations: Real machine learning schemes
6.1 Decision trees
6.2 Classification rules
6.3 Extending linear models
6.4 Instance-based learning
6.5 Numeric prediction
6.6 Clustering
6.7 Bayesian networks

7. Transformations: Engineering the input and output
7.1 Attribute selection
7.2 Discretizing numeric attributes
7.3 Some useful transformations
7.4 Automatic data cleansing
7.5 Combining multiple models
7.6 Using unlabeled data
7.7 Further reading

8. Moving on: Extensions and applications
8.1 Learning from massive datasets
8.2 Incorporating domain knowledge
8.3 Text and Web mining
8.4 Adversarial situations
8.5 Ubiquitous data mining
8.6 Further reading

Part II: The Weka machine learning workbench

9. Introduction to Weka
9.1 What’s in Weka?
9.2 How do you use it?
9.3 What else can you do?

10. The Explorer
10.1 Getting started
10.2 Exploring the Explorer
10.3 Filtering algorithms
10.4 Learning algorithms
10.5 Meta-learning algorithms
10.6 Clustering algorithms
10.7 Association-rule learners
10.8 Attribute selection

11. The Knowledge Flow interface
11.1 Getting started
11.2 Knowledge Flow components
11.3 Configuring and connecting the components
11.4 Incremental learning

12. The Experimenter
12.1 Getting started
12.2 Simple setup
12.3 Advanced setup
12.4 The Analyze panel
12.5 Distributing processing over several machines

13. The command-line interface
13.1 Getting started
13.2 The structure of Weka
13.3 Command-line options

14. Embedded machine learning

15. Writing new learning schemes


Purchase Data Mining: Practical Machine Learning Tools and Techniques (Second Edition) from
Dear user! You need to be registered and logged in to fully enjoy We recommend registering or logging in.

Tags: Second, techniques, different, Mining, Techniques, techniques, machine, Shows, methods