Web Data Mining

Exploring Hyperlinks, Contents, and Usage Data

  • Bing┬áLiu

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

  • Bing┬áLiu
    • 1
  1. 1.Dept. Computer ScienceUniversity of IllinoisChicagoUSA

Bibliographic information