Synopsis
Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today’s techniques coupled with the methods at the leading edge of contemporary research
- Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
- Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
- Features in-depth information on deep learning and probabilistic models
- Covers performance improvement techniques, including input preprocessing and combining output from different methods
- Provides an appendix introducing the WEKA machine learning workbench and links to algorithm implementations in the software
- Includes all-new exercises for each chapter
About the Authors
Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography.
Eibe Frank lives in New Zealand with his Samoan spouse and two lovely boys, but originally hails from Germany, where he received his first degree in computer science from the University of Karlsruhe. He moved to New Zealand to pursue his Ph.D. in machine learning under the supervision of Ian H. Witten and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now a professor at the same institution. As an early adopter of the Java programming language, he laid the groundwork for the Weka software described in this book. He has contributed a number of publications on machine learning and data mining to the literature and has refereed for many conferences and journals in these areas.
Mark A. Hall holds a bachelor’s degree in computing and mathematical sciences and a Ph.D. in computer science, both from the University of Waikato. Throughout his time at Waikato, as a student and lecturer in computer science and more recently as a software developer and data mining consultant for Pentaho, an open-source business intelligence software company, Mark has been a core contributor to the Weka software described in this book. He has published several articles on machine learning and data mining and has refereed for conferences and journals in these areas.
Christopher J. Pal is a Canada CIFAR AI Chair and a full professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. Pal’s research interests include computer vision and pattern recognition, computational photography, natural language processing, statistical machine learning and applications to human computer interaction.
Dr. James (Jimmy) Foulds is an associate professor in the Department of Information Systems at the University of Maryland, Baltimore County. Previously, he was a postdoctoral scholar at the University of California, San Diego under the Data Science Postdoctoral Fellowship program, co-sponsored by ITA, Calit2, the Qualcomm Institute, CSE and ECE. Prior to that he was a postdoctoral scholar in Lise Getoor's LINQS research group at UCSC, and he graduated from Padhraic Smyth's DataLab group at UCI. Dr. Foulds' research interests are broadly in socially conscious machine learning and artificial intelligence. His work aims to improve AI’s role in society regarding fairness and privacy, and to promote the practice of computational social science, using probabilistic models and Bayesian inference.
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