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HAN

Foreword to Second Edition

We are deluged by data—scientific data, medical data, demographic data, financial data,
and marketing data. People have no time to look at this data. Human attention has
become the precious resource. So, we must find ways to automatically analyze the
data, to automatically classify it, to automatically summarize it, to automatically dis-
cover and characterize trends in it, and to automatically flag anomalies. This is one
of the most active and exciting areas of the database research community. Researchers
in areas including statistics, visualization, artificial intelligence, and machine learning
are contributing to this field. The breadth of the field makes it difficult to grasp the
extraordinary progress over the last few decades.
Six years ago, Jiawei Han’s and Micheline Kamber’s seminal textbook organized and
presented Data Mining. It heralded a golden age of innovation in the field. This revision
of their book reflects that progress; more than half of the references and historical notes
are to recent work. The field has matured with many new and improved algorithms, and
has broadened to include many more datatypes: streams, sequences, graphs, time-series,
geospatial, audio, images, and video. We are certainly not at the end of the golden age—
indeed research and commercial interest in data mining continues to grow—but we are
all fortunate to have this modern compendium.
The book gives quick introductions to database and data mining concepts with
particular emphasis on data analysis. It then covers in a chapter-by-chapter tour the
concepts and techniques that underlie classification, prediction, association, and clus-
tering. These topics are presented with examples, a tour of the best algorithms for each
problem class, and with pragmatic rules of thumb about when to apply each technique.
The Socratic presentation style is both very readable and very informative. I certainly
learned a lot from reading the first edition and got re-educated and updated in reading
the second edition.
Jiawei Han and Micheline Kamber have been leading contributors to data mining
research. This is the text they use with their students to bring them up to speed on

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