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Preface

The computerization of our society has substantially enhanced our capabilities for both
generating and collecting data from diverse sources. A tremendous amount of data has
flooded almost every aspect of our lives. This explosive growth in stored or transient
data has generated an urgent need for new techniques and automated tools that can
intelligently assist us in transforming the vast amounts of data into useful information
and knowledge. This has led to the generation of a promising and flourishing frontier
in computer science called data mining, and its various applications. Data mining, also
popularly referred to as knowledge discovery from data (KDD), is the automated or con-
venient extraction of patterns representing knowledge implicitly stored or captured in
large databases, data warehouses, the Web, other massive information repositories, or
data streams.
This book explores the concepts and techniques of knowledge discovery and data min-
ing.Asamultidisciplinaryfield,dataminingdrawsonworkfromareasincludingstatistics,
machine learning, pattern recognition, database technology, information retrieval,
network science, knowledge-based systems, artificial intelligence, high-performance
computing, and data visualization. We focus on issues relating to the feasibility, use-
fulness, effectiveness, and scalability of techniques for the discovery of patterns hidden
in large data sets. As a result, this book is not intended as an introduction to statis-
tics, machine learning, database systems, or other such areas, although we do provide
some background knowledge to facilitate the reader’s comprehension of their respective
roles in data mining. Rather, the book is a comprehensive introduction to data mining.
It is useful for computing science students, application developers, and business
professionals, as well as researchers involved in any of the disciplines previously listed.
Data mining emerged during the late 1980s, made great strides during the 1990s, and
continues to flourish into the new millennium. This book presents an overall picture
of the field, introducing interesting data mining techniques and systems and discussing
applications and research directions. An important motivation for writing this book was
the need to build an organized framework for the study of data mining—a challenging
task, owing to the extensive multidisciplinary nature of this fast-developing field. We
hope that this book will encourage people with different backgrounds and experiences
to exchange their views regarding data mining so as to contribute toward the further
promotion and shaping of this exciting and dynamic field.

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