Mining the Web: Discovering Knowledge from Hypertext Data

Přední strana obálky
Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for extracting and producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issues-including Web crawling and indexing-Chakrabarti examines machine learning techniques as they relate specifically to the challenges of Web mining and provides applications of machine learning to sytematically acquire, store, and analyze data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress toward a Web that is more aware of content semantics. This thorough and forward-looking book gives the theoretical and practical foundations you need to build innovative applications for mining the Web.

Features
  • A comprehensive, critical exploration of statistics-based attempts to make sense of Web data.
  • Details the special challenges associated with analyzing unstructured and semi-structured data.
  • Looks at how classical Information Retrieval techniques have been modified for use with Web data.
  • Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning.
  • Analyzes current applications for resource discovery and social network analysis.
  • An excellent way to introduce students to especially vital applications of data mining and machine learning technology.
 

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Obsah

Infrastructure
15
Learning
77
Applications
201
References
307
Index
327
About the Author
345
Autorská práva

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Oblíbené pasáže

Strana 312 - S. Deerwester, ST Dumais, TK Landauer, GW Furnas, and RA Harshman. "Indexing by latent semantic analysis," Journal of the Society for Information Science.
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Strana 312 - S. Dumais, J. Platt, D. Heckerman, and M. Sahami. Inductive learning algorithms and representations for text categorization.
Strana i - Management of Heterogeneous and Autonomous Database Systems Edited by Ahmed Elmagarmid, Marek Rusinkiewicz, and Amit Sheth Object-Relational DBMSs: Tracking the Next Great Wave, Second Edition Michael Stonebraker and Paul Brown, with Dorothy Moore A Complete Guide to DB2 Universal Database Don Chamberlin Universal Database Management: A Guide to Object/Relational Technology Cynthia Maro Saracco Readings in Database Systems, Third Edition Edited by Michael Stonebraker and Joseph M.
Strana i - Understanding Relational Language Components Jim Melton and Alan R. Simon Information Visualization in Data Mining and Knowledge Discovery Edited by Usama Fayyad, Georges G. Grinstein, and Andreas Wierse...
Strana ii - Models for Advanced Applications, Edited by Ahmed K. Elmagarmid A Guide to Developing Client/Server SQL Applications, Setrag Khoshafian. Arvola Chan, Anna Wong, and Harry KT Wong The Benchmark Handbook for Database and Transaction Processing Systems, Second Edition, Edited by Jim Gray Camelot and Avalon: A Distributed Transaction Facility, Edited by Jeffrey L. Eppinger, Lily B. Mummert, and Alfred Z. Spector Readings in Object-Oriented Database Systems, Edited by Stanley B. Zdonik and David Maier...
Strana 311 - DR Cutting, DR Karger, JO Pedersen, and JW Tukey. Scatter/gather: a cluster-based approach to browsing large document collections.

O autorovi (2002)

Soumen Chakrabarti is assistant Professor in Computer Science and Engineering at the Indian Institute of Technology, Bombay. Prior to joining IIT, he worked on hypertext databases and data mining at IBM Almaden Research Center. He has developed three systems and holds five patents in this area. Chakrabarti has served as a vice-chair and program committee member for many conferences, including WWW, SIGIR, ICDE, and KDD, and as a guest editor of the IEEE TKDE special issue on mining and searching the Web. His work on focused crawling received the Best Paper award at the 8th International World Wide Web Conference (1999). He holds a Ph.D. from the University of California, Berkeley.

Bibliografické údaje