Data Mining: Methodologies and Applications presents a comprehensive yet accessible introduction to data mining-a key area of artificial intelligence focused on uncovering patterns and insights from datasets using machine learning techniques. This textbook addresses a gap in existing literature by offering a clear, balanced approach that integrates theoretical foundations with real-world applications.
Core topics include essential data mining methods such as regression, classification, clustering, and association analysis. The book goes beyond algorithm application to explain how and why these techniques work, equipping students with the knowledge needed to make informed decisions in data-driven environments.
Designed for advanced undergraduate and graduate students, particularly in engineering, computer science, and related STEM fields, this book includes hands-on exercises, case studies, and projects. These features reinforce concepts and challenge readers to apply data mining methods to realistic problems-preparing them for the demands of AI-driven industries.
As artificial intelligence becomes increasingly integral to modern innovation, this book serves as a timely and practical guide for learners seeking to understand the methods that power intelligent systems.
Xuemin Jin holds a Ph.D. in physics from the University of Maryland, College Park. He is a Teaching Professor of Mechanical and Industrial Engineering and Director of the Data Analytics Engineering graduate program at Northeastern University.
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