© 2007

Web Data Mining

Exploring Hyperlinks, Contents, and Usage Data

  • Covers all key tasks and techniques of Web search and Web mining, i.e., structure mining, content mining, and usage mining

  • First book with such a comprehensive coverage

  • Includes major algorithms from data mining, machine learning, information retrieval and text processing, which are crucial for many Web mining tasks

  • First self-contained book

  • Contains a rich blend of theory and practice, addressing seminal research ideas and also looking at the technology from a practical point of view

  • Ideally suited for classes on Data Mining, Web Mining, Web Search, and Knowledge Discovery in Data Bases

  • Provides internet support with lecture slides and project problems


Part of the Data-Centric Systems and Applications book series (DCSA)

Table of contents

  1. Front Matter
    Pages I-XIX
  2. Introduction

    1. Pages 1-12
  3. Data Mining Foundations

  4. Web Mining

  5. Back Matter
    Pages 485-532

About this book


The rapid growth of the Web in the last decade makes it the largest p- licly accessible data source in the world. Web mining aims to discover u- ful information or knowledge from Web hyperlinks, page contents, and - 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 m- 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 - gorithms. The book is intended to be a text with a comprehensive cov- 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.


Perl Web Crawling Web Data Mining algorithms data mining learning machine learning web mining

Authors and affiliations

  1. 1.Dept. Computer ScienceUniversity of IllinoisChicagoUSA

About the authors

Bing Liu is an associate professor in Computer Science at the University of Illinois at Chicago (UIC). He received his PhD degree in Artificial Intelligence from the University of Edinburgh. Before joining UIC in 2002, he was with the National University of Singapore. His research interests include data mining, Web mining, text mining, and machine learning. He has published extensively in these areas in leading conferences and journals. He served (or serves) as a vice chair, deputy vice chair or program committee member of many conferences, including WWW, KDD, ICML, VLDB, ICDE, AAAI, SDM, CIKM and ICDM.

Bibliographic information

Industry Sectors
IT & Software
Finance, Business & Banking


From the reviews:

"This is a textbook about data mining and its application to the Web. … Liu succeeds in helping readers appreciate the key role that data mining and machine learning play in Web applications. … It also motivates the student by adding immediacy and relevance to the concepts and algorithms described. I liked the way the concepts are introduced in a stepwise manner. … I also appreciated the bibliographical notes at the end of each chapter." (W. Hu, ACM Computing Reviews, January, 2009)