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Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data Hardback - 2011
by Bing Liu
- New
- Hardcover
Description
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Details
- Title Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data
- Author Bing Liu
- Binding Hardback
- Edition 2nd ed. 2011
- Condition New
- Pages 624
- Volumes 1
- Language ENG
- Publisher Springer
- Date 2011-06-26
- Features Bibliography, Index, Table of Contents, Textbook
- Bookseller's Inventory # B9783642194597
- ISBN 9783642194597 / 3642194591
- Weight 2.4 lbs (1.09 kg)
- Dimensions 9.5 x 6.8 x 1.6 in (24.13 x 17.27 x 4.06 cm)
- Library of Congress Catalog Number 2011932320
- Dewey Decimal Code 006.3
From the publisher
From the rear cover
Web mining aims to discover useful information and knowledge from Web hyperlinks, 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 semi-structured and unstructured nature of the Web data. The field has also developed many of its own algorithms and techniques.
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 therelated 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.
Media reviews
Citations
- Choice, 01/01/2012, Page 0