Data Mining
Practical Machine Learning Tools and Techniques
by Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal, James Foulds
5th Edition
Publisher: Morgan Kaufmann
Book Details
| Print ISBN | 9780443158889 |
| eText ISBN | 9780443158896 |
| Publisher | Morgan Kaufmann |
| Publishing Year | 2025 |
| Edition | 5th Edition |
| Language | English |
| Pages | 688 |
Data Mining: Practical Machine Learning Tools and Techniques, 5th Edition is a computer science textbook covering machine learning concepts and practical analytical techniques. The text pairs conceptual instruction with practical advice for real-world data mining applications.
The volume structures its material across two primary sections. Part I details data mining concepts, input principles, knowledge representation, basic algorithmic methods, credibility evaluation, data preparation, and ethical considerations. Part II expands into advanced machine learning schemes, covering ensemble learning methods, deep learning frameworks, probabilistic approaches, and practical application impacts.
Honored with the 2026 Textbook and Academic Authors Association Textbook Excellence "Texty" Award, this fifth edition provides structured instruction that supports study across computational curricula and technical workflows.
Table of Contents
Chapter 1: What’s it all about?
Chapter 2: Input: concepts, instances, attributes
Chapter 3: Output: knowledge representation
Chapter 4: Algorithms: the basic methods
Chapter 5: Credibility: evaluating what’s been learned
Chapter 6: Preparation: data preprocessing and exploratory data analysis
Chapter 7: Ethics: what are the impacts of what's been learned?
Chapter 8: Ensemble learning
Chapter 9: Extending instance-based and linear models
Chapter 10: Deep learning: fundamentals
Chapter 11: Advanced deep learning methods
Chapter 12: Beyond supervised and unsupervised learning
Chapter 13: Probabilistic methods: fundamentals
Chapter 14: Advanced probabilistic methods
Chapter 15: Moving on: applications and their consequences
Chapter A: Theoretical foundations
Chapter B: The WEKA workbench
Chapter C: Implementation details of trees and rules
Chapter D: Technical details of deep learning
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- Data mining practical machine learning tools and techniques.
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