book

Data Mining, 2nd Edition

by Ian H. Witten, Eibe Frank

July 2005

Beginner to intermediate

560 pages

16h 32m

English

Morgan Kaufmann

Read now

Unlock full access

1.1 Data mining and machine learning1.2 Simple examples: The weather problem and others1.3 Fielded applications1.4 Machine learning and statistics1.5 Generalization as search1.6 Data mining and ethics1.7 Further reading
2.1 What's a concept?2.2 What's in an example?2.3 What's in an attribute?2.4 Preparing the input2.5 Further reading
3.1 Decision tables3.2 Decision trees3.3 Classification rules3.4 Association rules3.5 Rules with exceptions3.6 Rules involving relations3.7 Trees for numeric prediction3.8 Instance-based representation3.9 Clusters3.10 Further reading
4.1 Inferring rudimentary rules4.2 Statistical modeling4.3 Divide-and-conquer: Constructing decision trees4.5 Mining association rules4.6 Linear models4.7 Instance-based learning4.8 Clustering4.9 Further reading
5.1 Training and testing5.2 Predicting performance5.3 Cross-validation5.4 Other estimates5.5 Comparing data mining methods5.6 Predicting probabilities5.7 Counting the cost5.8 Evaluating numeric prediction5.9 The minimum description length principle5.10 Applying the MDL principle to clustering5.11 Further reading
6.1 Decision trees6.2 Classification rules6.3 Extending linear models6.4 Instance-based learning6.5 Numeric prediction6.6 Clustering6.7 Bayesian networks
7.1 Attribute selection7.2 Discretizing numeric attributes7.3 Some useful transformations7.4 Automatic data cleansing7.5 Combining multiple models7.6 Using unlabeled data7.7 Further reading
8.1 Learning from massive datasets8.2 Incorporating domain knowledge8.3 Text and Web mining8.4 Adversarial situations8.5 Ubiquitous data mining8.6 Further reading
9.1 What's in Weka?9.2 How do you use it?9.3 What else can you do?9.4 How do you get it?
10.1 Getting started10.2 Exploring the Explorer10.3 Filtering algorithms10.4 Learning algorithms10.5 Metalearning algorithms10.6 Clustering algorithms10.7 Association-rule learners10.8 Attribute selection
11.1 Getting started11.2 The Knowledge Flow components11.3 Configuring and connecting the components11.4 Incremental learning
12.1 Getting started12.2 Simple setup12.3 Advanced setup12.4 The Analyze panel12.5 Distributing processing over several machines
13.1 Getting started13.2 The structure of Weka13.3 Command-line options
14.1 A simple data mining application14.2 Going through the code
15.1 An example classifier15.2 Conventions for implementing classifiers

Overview

Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references.

The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more.

This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses.

Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods
Performance improvement techniques that work by transforming the input or output