Page 27 -

xxvi Preface HAN 05-pref-xxiii-xxx-9780123814791 2011/6/1 3:35 Page xxvi #4

Chapter 12 is dedicated to outlier detection. It introduces the basic concepts of out-
liers and outlier analysis and discusses various outlier detection methods from the view
of degree of supervision (i.e., supervised, semi-supervised, and unsupervised meth-
ods), as well as from the view of approaches (i.e., statistical methods, proximity-based
methods, clustering-based methods, and classification-based methods). It also discusses
methods for mining contextual and collective outliers, and for outlier detection in
high-dimensional data.
Finally, in Chapter 13, we discuss trends, applications, and research frontiers in data
mining. We briefly cover mining complex data types, including mining sequence data
(e.g., time series, symbolic sequences, and biological sequences), mining graphs and
networks, and mining spatial, multimedia, text, and Web data. In-depth treatment of
data mining methods for such data is left to a book on advanced topics in data mining,
the writing of which is in progress. The chapter then moves ahead to cover other data
mining methodologies, including statistical data mining, foundations of data mining,
visual and audio data mining, as well as data mining applications. It discusses data
mining for financial data analysis, for industries like retail and telecommunication, for
use in science and engineering, and for intrusion detection and prevention. It also dis-
cusses the relationship between data mining and recommender systems. Because data
mining is present in many aspects of daily life, we discuss issues regarding data mining
and society, including ubiquitous and invisible data mining, as well as privacy, security,
and the social impacts of data mining. We conclude our study by looking at data mining
trends.
Throughout the text, italic font is used to emphasize terms that are defined, while
bold font is used to highlight or summarize main ideas. Sans serif font is used for
reserved words. Bold italic font is used to represent multidimensional quantities.
This book has several strong features that set it apart from other texts on data mining.
It presents a very broad yet in-depth coverage of the principles of data mining. The
chapters are written to be as self-contained as possible, so they may be read in order of
interest by the reader. Advanced chapters offer a larger-scale view and may be considered
optional for interested readers. All of the major methods of data mining are presented.
The book presents important topics in data mining regarding multidimensional OLAP
analysis, which is often overlooked or minimally treated in other data mining books.
The book also maintains web sites with a number of online resources to aid instructors,
students, and professionals in the field. These are described further in the following.

To the Instructor

This book is designed to give a broad, yet detailed overview of the data mining field. It
can be used to teach an introductory course on data mining at an advanced undergrad-
uate level or at the first-year graduate level. Sample course syllabi are provided on the
book’s web sites (www.cs.uiuc.edu/∼hanj/bk3 and www.booksite.mkp.com/datamining3e)
in addition to extensive teaching resources such as lecture slides, instructors’ manuals,
and reading lists (see p. xxix).