D A T A M I N I N G A N D A N A L Y S I S
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 d ata mining courses
provides a broad yet i n -depth overview of data mining, integrating related
concepts from machine learning and statistics. Th e 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.
Key Features:
•
Covers both core methods and cutting-edge research
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Algorithmic approach with open-source implementations
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Minimal prerequisites, as all key mathematical concepts are
presented, as is the intuition behind the formulas
•
Short, self-contained chapters with class-tested examples and
exercises that allow for fl exibility in designing a cours e and for easy
reference
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Supplementary online resource containing lecture slides, videos,
project ideas, and more
Mohammed J. Zaki is a Professor of Computer Science at Rensselaer
Polytechnic Institute, Troy, New York.
Wagner Meira Jr. is a Professor of Computer Science at Universidade
Federal de Minas Gerais, Brazil.
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