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Web Data Mining

Exploring Hyperlinks, Contents, and Usage Data

By Bing Liu

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This is the first book to provide such a comprehensive text on Web data mining. The book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text.

Full Description

  • ISBN13: 978-3-5403-7881-5
  • 556 Pages
  • User Level: Students
  • Publication Date: May 30, 2007
  • Available eBook Formats: PDF
  • eBook Price: $59.95
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Full Description
Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semistructured and unstructured nature of the Web data and its heterogeneity. It has also developed many of its own algorithms and techniques. Liu has written a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered both in breadth and in depth. His book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text. The book offers a rich blend of theory and practice, addressing seminal research ideas, as well as examining the technology from a practical point of view. It is suitable for students, researchers and practitioners interested in Web mining both as a learning text and a reference book. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.
Table of Contents

Table of Contents

  1. 1) Introduction
  2. 2) Association Rules and Sequential Patterns
  3. 3) Supervised Learning
  4. 4) Unsupervised Learning
  5. 5) Partially Supervised Learning
  6. 6) Information Retrieval and Web Search
  7. 7) Link Analysis
  8. 8) Web Crawling
  9. 9) Structured Data Extraction: Wrapper Generation
  10. 10) Information Integration
  11. 11) Opinion Mining
  12. 12) Web Usage Mining
  13. References, Index
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