Book: Data Mining and Analysis: Fundamental Concepts and Algorithms
This textbook for senior undergraduate and graduate data mining courses provides a broad yet indepth overview of data mining, integrating related concepts from machine learning and statistics. Companion website has data, slides and other teaching material.
By Mohammed J. Zaki, May 27, 2014.
We are pleased to announce the publication and availability of our textbook on data mining and data science.
Data Mining and Analysis: Fundamental Concepts and Algorithms
by Mohammed J. Zaki and Wagner Meira, Jr.
Cambridge University Press, May 2014
See also
www.amazon.com/DataMiningAnalysisFundamentalAlgorithms/dp/0521766338/
Companion Website for Online Resources:
dataminingbook.info
Description & Features:
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 indepth 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 cuttingedge topics such as kernel methods, highdimensional 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.
Key features:
Reviews & Endorsements:
Gregory PiatetskyShapiro, President KDnuggets, Founder ACM SIGKDD, the leading professional organization for Knowledge Discovery and Data Mining
Professor Christos Faloutsos, Carnegie Mellon University and winner of the ACM SIGKDD Innovation Award
Related:
We are pleased to announce the publication and availability of our textbook on data mining and data science.
Data Mining and Analysis: Fundamental Concepts and Algorithms
by Mohammed J. Zaki and Wagner Meira, Jr.
Cambridge University Press, May 2014
See also
www.amazon.com/DataMiningAnalysisFundamentalAlgorithms/dp/0521766338/
Companion Website for Online Resources:
dataminingbook.info
Description & Features:
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 indepth 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 cuttingedge topics such as kernel methods, highdimensional 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.
Key features:
 Covers both core methods and cuttingedge research
 Algorithmic approach with opensource implementations
 Minimal prerequisites: all key mathematical concepts are presented, as is the intuition behind the formulas
 Short, selfcontained chapters with classtested examples and exercises allow for flexibility in designing a course and for easy reference
 Supplementary website with lecture slides, videos, project ideas, and more
Reviews & Endorsements:
Gregory PiatetskyShapiro, President KDnuggets, Founder ACM SIGKDD, the leading professional organization for Knowledge Discovery and Data Mining
"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."
Professor Christos Faloutsos, Carnegie Mellon University and winner of the ACM SIGKDD Innovation Award
"Worldclass 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."
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