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Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications) [Hardcover]

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Item description for Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications) by Bing Liu...

Web mining aims to discover useful information and knowledge from the Web hyperlink structure, 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. It 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 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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Item Specifications...


Pages   532
Est. Packaging Dimensions:   Length: 9.29" Width: 6.14" Height: 1.42"
Weight:   1.98 lbs.
Binding  Hardcover
Release Date   Aug 1, 2008
Publisher   Springer
ISBN  3540378812  
ISBN13  9783540378815  


Availability  0 units.


More About Bing Liu


Register your artisan biography and upload your photo! 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.

Bing Liu was born in 1963.

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Product Categories

1Books > Calendars > Arts
2Books > Special Features > New & Used Textbooks > Computer Science & Information Systems > Artificial Intelligence
3Books > Special Features > New & Used Textbooks > Computer Science & Information Systems > Database Storage & Design
4Books > Subjects > Computers & Internet > Computer Science > Artificial Intelligence > Computer Mathematics
5Books > Subjects > Computers & Internet > Computer Science > Artificial Intelligence > General
6Books > Subjects > Computers & Internet > Computer Science > Artificial Intelligence > Human Vision & Language Systems
7Books > Subjects > Computers & Internet > Computer Science > Artificial Intelligence > Machine Learning
8Books > Subjects > Computers & Internet > Databases > Data Storage & Management > Data Mining
9Books > Subjects > Computers & Internet > Databases > General
10Books > Subjects > Computers & Internet > General
11Books > Subjects > Science > Mathematics > Applied > General



Reviews - What do customers think about Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)?

Excellent graduate text and reference  Mar 5, 2008
This book makes a great text for graduate courses, as well as a reference for scholars. The chapters are well written and provide good examples for any significant concepts. Each section covers the basics to establish a foundation of understanding for someone unfamiliar with the area, but goes on to also touch upon the research forefront on each topic. One of the most useful sections I've found as a researcher is the Bibliographic Notes found at the end of each section which briefly describes the major groups of work within the topic with cites to major papers/articles/books in each of these areas (seems to be about 50 or so per chapter).

The only "drawback" to this book would be if you wanted to touch upon everything, there is far too much content for a single semester. However as mentioned above, the chapters are structured such that you could easily use the first couple sections of each chapter to cover all the foundations and either leave later sections for students to read on their own/select an advanced project, or cover the remainder in a 2nd semester.

I highly recommend this book to any graduate looking for a comprehensive text and reference on web mining.

(In the interest of full disclosure, I am listed in the acknowledgements from providing feedback on a pre-print edition of the text that was used as our course textbook. I do not get royalties from sales in any way.)
 

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