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Data Mining and Machine Learning: Fundamental Concepts and Algorithms by Mohammed J. Zaki (Rensselaer Polytechnic Institute, New York) at Abbey's Bookshop,
Data Mining and Machine Learning: Fundamental Concepts and Algorithms
— —
Mohammed J. Zaki (Rensselaer Polytechnic Institute, New York) Wagner Meira, Jr (Universidade Federal de Minas Gerais, Brazil)

Data Mining and Machine Learning

Fundamental Concepts and Algorithms

Mohammed J. Zaki (Rensselaer Polytechnic Institute, New York),  Wagner Meira, Jr (Universidade Federal de Minas Gerais, Brazil)

$114.95

Hardback

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English
Cambridge University Press
30 January 2020
Knowledge management; Data mining; Machine learning; Pattern recognition
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.
By:   , , ,
Imprint:   Cambridge University Press
Country of Publication:   United Kingdom
Edition:   2nd Revised edition
Dimensions:   Height: 257mm,  Width: 185mm,  Spine: 45mm
Weight:   1.600kg
ISBN:   9781108473989
ISBN 10:   1108473989
Pages:   776
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active

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 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.

Reviews for Data Mining and Machine Learning: Fundamental Concepts and Algorithms

'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.' Gregory Piatetsky-Shapiro, Founder of the Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining (ACM SIGKDD) 'World-class experts, providing an encyclopedic coverage of all datamining topics, from basic statistics to fundamental methods (clustering, classification, frequent itemsets), to advanced methods (SVD, SVM, kernels, spectral graph theory, deep learning). 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.' Christos Faloutsos, Carnegie Mellon University, Pennsylvania, and winner of the ACM SIGKDD Innovation Award


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