Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data by Bing LiuWeb Data Mining: Exploring Hyperlinks, Contents, and Usage Data by Bing Liu

Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data

byBing Liu

Paperback | August 3, 2013

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Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. 

The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.

Bing Liu is a professor of Computer Science at the University of Illinois at Chicago (UIC). He received his PhD in Artificial Intelligence from the University of Edinburgh. Before joining UIC, he was with the National University of Singapore. His current research interests include opinion mining and sentiment analysis, text and Web min...
Title:Web Data Mining: Exploring Hyperlinks, Contents, and Usage DataFormat:PaperbackDimensions:624 pagesPublished:August 3, 2013Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3642268919

ISBN - 13:9783642268915


Table of Contents

1. Introduction.- Part I: Data Mining Foundations.- 2. Association Rules and Sequential Patterns.- 3. Supervised Learning.- 4. Unsupervised Learning.- 5. Partially Supervised Learning.- Part II: Web Mining.- 6. Information Retrieval and Web Search.- 7. Social Network Analysis.- 8. Web Crawling.- 9. Structured Data Extraction: Wrapper Generation.- 10. Information Integration.- 11. Opinion Mining and Sentiment Analysis.- 12. Web Usage Mining.

Editorial Reviews

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 2009From 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 book handy 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