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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 thebasis of data science, utilizing automated methods to analyze patternsand models for all kinds of data in applications ranging from scientificdiscovery to business analytics. This textbook for senior undergraduateand graduate courses provides a comprehensive, in-depth overview of datamining, machine learning and statistics, offering solid guidance forstudents, researchers, and practitioners. The book lays the foundationsof data analysis, pattern mining, clustering, classification andregression, with a focus on the algorithms and the underlying algebraic,geometric, and probabilistic concepts. New to this second edition is anentire part devoted to regression methods, including neural networks anddeep 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), deeplearning (recurrent and convolutional neural networks), and regressionassessment.

Expanded material on ensemble models in chapter 24.

Math notation has been clarified, and important equations are nowboxed 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 andother materials for the new edition.

Description of the first edition is alsoavailable.

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.

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