Data Mining 5th Edition

Data Mining, 5th Edition by James Foulds; Ian H. Witten; Eibe Frank; Mark A. Hall; Christopher J. Pal is an essential, up-to-date guide for anyone serious about extracting insight from data. Clear, authoritative and practical, this edition blends rigorous theory with real-world techniques to make complex concepts accessible to students, data scientists, and industry practitioners worldwide.

Explore core topics—classification, clustering, association rules, feature selection, model evaluation, and scalable algorithms—presented with lucid explanations and contemporary examples. The authors balance mathematical foundation and hands-on intuition so you can understand when and why methods work, not just how to apply them. Updated discussions reflect advances in predictive analytics, machine learning workflows, and best practices for handling large, messy datasets common across business, healthcare, finance, and research.

Readers will appreciate the logical progression from basic principles to advanced strategies, supported by practical guidance on model validation, avoiding overfitting, and interpreting results for stakeholder communication. Whether you’re a graduate student, analyst, or technical manager, this edition equips you to design robust data-mining projects and translate patterns into actionable decisions.

Perfect for classrooms and professional reference shelves across North America, Europe, Asia, and beyond, Data Mining, 5th Edition stands out as a definitive resource. Strengthen your analytical toolkit and gain confidence turning big data into measurable value—add this indispensable volume to your collection today.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.