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Preface xxvii

Chapter 6.
Chapter 2. Mining Chapter 10.
Chapter 1. Getting to Chapter 3. Frequent Chapter 8. Cluster
Data
Classification:
Introduction Know Your Preprocessing Patterns, .... Basic Concepts Analysis: Basic
Concepts and
Data Basic Methods
Concepts ...
Figure P.1 A suggested sequence of chapters for a short introductory course.

Depending on the length of the instruction period, the background of students, and
your interests, you may select subsets of chapters to teach in various sequential order-
ings. For example, if you would like to give only a short introduction to students on data
mining, you may follow the suggested sequence in Figure P.1. Notice that depending on
the need, you can also omit some sections or subsections in a chapter if desired.
Depending on the length of the course and its technical scope, you may choose to
selectively add more chapters to this preliminary sequence. For example, instructors
who are more interested in advanced classification methods may first add “Chapter 9.
Classification: Advanced Methods”; those more interested in pattern mining may choose
to include “Chapter 7. Advanced Pattern Mining”; whereas those interested in OLAP
and data cube technology may like to add “Chapter 4. Data Warehousing and Online
Analytical Processing” and “Chapter 5. Data Cube Technology.”
Alternatively, you may choose to teach the whole book in a two-course sequence that
covers all of the chapters in the book, plus, when time permits, some advanced topics
such as graph and network mining. Material for such advanced topics may be selected
from the companion chapters available from the book’s web site, accompanied with a
set of selected research papers.
Individual chapters in this book can also be used for tutorials or for special topics in
related courses, such as machine learning, pattern recognition, data warehousing, and
intelligent data analysis.
Each chapter ends with a set of exercises, suitable as assigned homework. The exer-
cises are either short questions that test basic mastery of the material covered, longer
questions that require analytical thinking, or implementation projects. Some exercises
can also be used as research discussion topics. The bibliographic notes at the end of each
chapter can be used to find the research literature that contains the origin of the concepts
and methods presented, in-depth treatment of related topics, and possible extensions.

To the Student

We hope that this textbook will spark your interest in the young yet fast-evolving field of
data mining. We have attempted to present the material in a clear manner, with careful
explanation of the topics covered. Each chapter ends with a summary describing the
main points. We have included many figures and illustrations throughout the text to
make the book more enjoyable and reader-friendly. Although this book was designed as
a textbook, we have tried to organize it so that it will also be useful to you as a reference