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- Hardcover: 532 pages
- Publisher: Springer; 1st ed. 2007. Corr. 2nd printing edition (January 21, 2009)
- Language: English
- ISBN-10: 3540378812
- ISBN-13: 978-3540378815
Product Description
Web mining aims to discover usefulinformation and knowledge from the Web hyperlink structure, pagecontents, and usage data. Although Web mining uses many conventionaldata mining techniques, it is not purely an application of traditionaldata mining due to the semistructured and unstructured nature of theWeb data and its heterogeneity. It has also developed many of its ownalgorithms and techniques. Liu has written a comprehensive text on Webdata mining. Key topics of structure mining, content mining, and usagemining are covered both in breadth and in depth. His book bringstogether all the essential concepts and algorithms from related areassuch as data mining, machine learning, and text processing to form anauthoritative and coherent text. The book offers a rich blend of theoryand practice, addressing seminal research ideas, as well as examiningthe technology from a practical point of view. It is suitable forstudents, researchers and practitioners interested in Web mining bothas a learning text and a reference book. Lecturers can readily use itfor classes on data mining, Web mining, and Web search. Additionalteaching materials such as lecture slides, datasets, and implementedalgorithms are available online.
About the Author
Bing Liu is an associateprofessor in Computer Science at the University of Illinois at Chicago(UIC). He received his PhD degree in Artificial Intelligence from theUniversity of Edinburgh. Before joining UIC in 2002, he was with theNational University of Singapore. His research interests include datamining, Web mining, text mining, and machine learning. He has publishedextensively in these areas in leading conferences and journals. Heserved (or serves) as a vice chair, deputy vice chair or programcommittee member of many conferences, including WWW, KDD, ICML, VLDB,ICDE, AAAI, SDM, CIKM and ICDM.
Content:
- Introduction
- Association Rules and Sequential Patterns
- Supervised Learning
- Unsupervised Learning
- Partially Supervised Learning
- Information Retrieval and Web Search
- Link Analysis
- Web Crawling
- Structured Data Extraction: Wrapper Generation
- Information Integration
- Opinion Mining
- Web Usage Mining
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