Web Data Mining : Exploring Hyperlinks, Contents, and Usage Data
Language: English
Published by Springer, 2011
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - 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.…
Seller Inventory # 9783642194597
- Title
- Web Data Mining : Exploring Hyperlinks, Contents, and Usage Data
- Author
- Bing Liu
- Publisher
- Springer
- Publication year
- 2011
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3642194591
- ISBN 13
- 9783642194597
- Edition
- 2nd Edition
- Item weight
- 1,121 grams
- Dimensions
- 241x160x40 mm
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.
"Synopsis" may belong to another edition of this title.
About the Author
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 mining, data mining, and machine learning. He has published extensively in top journals and conferences in these fields. Several of his publications are considered seminal papers of the fields and are highly cited. He has also given more than 30 keynote and invited talks in academia and in industry. On professional services, Liu has served as associate editors of IEEE Transactions on Knowledge and Data Engineering (TKDE), Journal of Data Mining and Knowledge Discovery (DMKD), and SIGKDD Explorations, and is on the editorial boards of several other journals. He has also served as program chairs of IEEE International Conference on Data Mining (ICDM-2010), ACM Conference on Web Search and Data Mining (WSDM-2010), ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008), SIAM Conference on Data Mining (SDM-2007), ACM Conference on Information and Knowledge Management (CIKM-2006), and Pacific Asia Conference on Data Mining (PAKDD-2002). Additionally, Liu has served extensively as area chairs and program committee members of leading conferences on data mining, Web mining, natural language processing, and machine learning. More information about him can be found from http://www.cs.uic.edu/~liub.
"About the title" may belong to another edition of this title.
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