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 andup 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 TechnologyThis 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
