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Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data / Edition 1

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Overview

Web mining aims to discover useful information and knowledge from Web hyperlink structures, 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. The field 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 as 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.

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Editorial Reviews

From the Publisher

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." ACM Computing Reviews, W. Hu, , January 2009

From the reviews of the second edition:

“Liu (Univ. of Illinois, Chicago) discusses all three types of Web mining—structure, content, and usage—in the technology’s efforts to glean information from hyperlinks, Web page content, and usage logs. […] Practical examples complement the discussions throughout the text, and each chapter includes useful ‘Bibliographic Notes’ and an extensive bibliography. […] Liu states that his intended audience includes both undergraduate and graduate students, but notes that researchers and Web programmers could benefit from this text as well. Summing Up: Recommended. Upper-division undergraduates through professionals.” J. Johnson, Choice, Vol. 49 (5), January 2012

"[...] Liu's book provides a comprehensive, self-contained introduction to the major data mining techniques and their use in Web data mining. [...] Professionals and researchers alike will find this excellent bookhandy as a reference. Its extensive lists of references at the end of each chapter provide hundreds of pointers for further reading. As a textbook, it is also suitable for advanced undergraduate and graduate courses on Web mining; it is highly selfcontained and includes many easy-to-understand examples that will help readers grasp the key ideas behind current Web data mining techniques." ACM Computing Reviews, Fernando Berzal, February 2012

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Product Details

  • ISBN-13: 9783540378815
  • Publisher: Springer-Verlag New York, LLC
  • Publication date: 1/21/2009
  • Series: Data-Centric Systems and Applications Series
  • Edition description: 1st ed. 2007. Corr. 2nd printing
  • Edition number: 1
  • Pages: 552
  • Sales rank: 475,369
  • Product dimensions: 1.19 (w) x 9.21 (h) x 6.14 (d)

Meet the Author

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.

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Table of Contents

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