Data Mining: Practical Machine Learning Tools and Techniques is a textbook written by Ian H. Witten and colleagues, intended for professionals and students who want to delve into machine learning and data mining. The book offers a thorough and practical introduction to machine learning, with attention to tools and techniques for analyzing and interpreting large datasets.
This fifth edition includes extensive updates on recent developments, such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT, GPT), and vulnerabilities arising from adversarial examples. In addition, it covers the ethical aspects and responsible use of artificial intelligence. The authors combine proven methods with contemporary research for an up-to-date overview of data mining.
Product specifications
- Author: Ian H. Witten (Department of Computer Science, University of Waikato, New Zealand)
- Publisher: Elsevier Science & Technology
- Imprint: Morgan Kaufmann Publishers
- Publication date: 2025-05-06
- Number of pages: 688
- ISBN: 9780443158889
- Theme: Data mining
- BISAC: COMPUTERS / Data Science / Data Analytics
About the author
Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. His research focuses on information storage, machine learning, text compression, and programming by demonstration. He is a member of the ACM and the Royal Society of New Zealand and has a broad publication history in the scientific field of digital libraries and artificial intelligence.