This textbook explores the different aspects of data mining from
the fundamentals to the complex data types and their applications,
capturing the wide diversity of problem domains for data mining
issues. It goes beyond the traditional focus on data mining
problems to introduce advanced data types such as text, time
series, discrete sequences, spatial data, graph data, and social
networks. Until now, no single book has addressed all these topics
in a comprehensive and integrated way. The chapters of this book
fall into one of three categories: Fundamental chapters: Data
mining has four main problems, which correspond to clustering,
classification, association pattern mining, and outlier analysis.
These chapters comprehensively discuss a wide variety of methods
for these problems. Domain chapters: These chapters discuss the
specific methods used for different domains of data such as text
data, time-series data, sequence data, graph data, and spatial
data. Application chapters: These chapters study important
applications such as stream mining, Web mining, ranking,
recommendations, social networks, and privacy preservation. The
domain chapters also have an applied flavor. Appropriate for both
introductory and advanced data mining courses, Data Mining: The
Textbook balances mathematical details and intuition. It contains
the necessary mathematical details for professors and researchers,
but it is presented in a simple and intuitive style to improve
accessibility for students and industrial practitioners (including
those with a limited mathematical background). Numerous
illustrations, examples, and exercises are included, with an
emphasis on semantically interpretable examples. Praise for Data
Mining: The Textbook - "As I read through this book, I have already
decided to use it in my classes. This is a book written by an
outstanding researcher who has made fundamental contributions to
data mining, in a way that is both accessible and up to date. The
book is complete with theory and practical use cases. It's a
must-have for students and professors alike!" -- Qiang Yang, Chair
of Computer Science and Engineering at Hong Kong University of
Science and Technology "This is the most amazing and comprehensive
text book on data mining. It covers not only the fundamental
problems, such as clustering, classification, outliers and frequent
patterns, and different data types, including text, time series,
sequences, spatial data and graphs, but also various applications,
such as recommenders, Web, social network and privacy. It is a
great book for graduate students and researchers as well as
practitioners." -- Philip S. Yu, UIC Distinguished Professor and
Wexler Chair in Information Technology at University of Illinois at
Chicago
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