Preface
The rapid growth of the Web in the last decade makes it the largest pub-
licly accessible data source in the world. Web mining aims to discover use-
ful information or knowledge from Web hyperlinks, page contents, and us-
age logs. Based on the primary kinds of data used in the mining process,
Web mining tasks can be categorized into three main types: Web structure
mining, Web content mining and Web usage mining. Web structure min-
ing discovers knowledge from hyperlinks, which represent the structure of
the Web. Web content mining extracts useful information/knowledge from
Web page contents. Web usage mining mines user access patterns from
usage logs, which record clicks made by every user.
The goal of this book is to present these tasks, and their core mining al-
gorithms. The book is intended to be a text with a comprehensive cover-
age, and yet, for each topic, sufficient details are given so that readers can
gain a reasonably complete knowledge of its algorithms or techniques
without referring to any external materials. Four of the chapters, structured
data extraction, information integration, opinion mining, and Web usage
mining, make this book unique. These topics are not covered by existing
books, but yet they are essential to Web data mining. Traditional Web
mining topics such as search, crawling and resource discovery, and link
analysis are also covered in detail in this book.
Although the book is entitled Web Data Mining, it also includes the
main topics of data mining and information retrieval since Web mining
uses their algorithms and techniques extensively. The data mining part
mainly consists of chapters on association rules and sequential patterns,
supervised learning (or classification), and unsupervised learning (or clus-
tering), which are the three most important data mining tasks. The ad-
vanced topic of partially (semi-) supervised learning is included as well.
For information retrieval, its core topics that are crucial to Web mining are
described. This book is thus naturally divided into two parts. The first part,
which consists of Chaps. 2–5, covers data mining foundations. The second
part, which contains Chaps. 6–12, covers Web specific mining.
Two main principles have guided the writing of this book. First, the ba-
sic content of the book should be accessible to undergraduate students, and
yet there are sufficient in-depth materials for graduate students who plan to
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