Main Page


Data Mining and Machine Learning: Fundamental Concepts and Algorithms
Second Edition
Mohammed J. Zaki and Wagner Meira, Jr
Cambridge University Press, March 2020
ISBN: 978-1108473989




The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

This second edition has the following new features and content:

  • New part five on regression: contains chapters on linear regression, logistic regression, neural networks (multilayer perceptrons), deep learning (recurrent and convolutional neural networks), and regression assessment.

  • Expanded material on ensemble models in chapter 24.

  • Math notation has been clarified, and important equations are now boxed for emphasis throughout the text.

  • Geometric view emphasized throughout the text, including for regression.

  • Errors from the first edition have been corrected.

You can find here the online book, errata, table of contents and resources like slides, videos and other materials for the new edition.

Description of the first edition is also available.


Mohammed J. Zaki, Rensselaer Polytechnic Institute, New York

Mohammed J. Zaki is Professor of Computer Science at Rensselaer Polytechnic Institute, New York, where he also serves as Associate Department Head and Graduate Program Director. He has more than 250 publications and is an Associate Editor for the journal Data Mining and Knowledge Discovery. He is on the Board of Directors for Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining (ACM SIGKDD). He has received the National Science Foundation CAREER Award, and the Department of Energy Early Career Principal Investigator Award. He is an ACM Distinguished Member, and IEEE Fellow.

Wagner Meira, Jr, Universidade Federal de Minas Gerais, Brazil

Wagner Meira, Jr is Professor of Computer Science at Universidade Federal de Minas Gerais, Brazil, where he is currently the chair of the department. He has published more than 230 papers on data mining and parallel and distributed systems. He was leader of the Knowledge Discovery research track of InWeb and is currently Vice-chair of INCT-Cyber. He is on the editorial board of the journal Data Mining and Knowledge Discovery and was the program chair of SDM'16 and ACM WebSci'19. He has been a CNPq researcher since 2002. He has received an IBM Faculty Award and several Google Faculty Research Awards.