The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.
Covers both core methods and cutting-edge research
Algorithmic approach with open-source implementations
Minimal prerequisites: all key mathematical concepts are presented, as is the intuition behind the formulas
Short, self-contained chapters with class-tested examples and exercises allow for flexibility in designing a course and for easy reference
Supplementary website with lecture slides, videos, project ideas, and more
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Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. ISBN: 9780521766333.
Reviews & Endorsements
This book by Mohammed Zaki and Wagner Meira, Jr. is a great option for teaching a course in data mining or data science. It covers both fundamental and advanced data mining topics, explains the mathematical foundations and the algorithms of data science, includes exercises for each chapter, and provides data, slides and other supplementary material on the companion website.
World-class experts, providing an encyclopedic coverage of all data mining topics, from basic statistics to fundamental methods (clustering, classification, frequent itemsets), to advanced methods (SVD, SVM, kernels, spectral graph theory). For each concept, the book thoughtfully balances the intuition, the arithmetic examples, as well the rigorous math details. It can serve both as a textbook, as well as a reference book.