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Web Mining: Extracting Knowledge from the World Wide Web

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Data Mining for Business Applications

This chapter addresses existing techniques for Web mining, which is moving the World Wide Web toward a more useful environment in which users can quickly and easily find the information they need. In particular, this chapter introduces the reader to methods of data mining on the Web developed by our laboratory, including uncovering patterns in Web content (semantic processing, classification, clustering), structure (retrieval, classical link analysis method), and event (preprocessing of Web event mining, news dynamic trace, multi-document summarization analysis). This chapter would be an excellent resource for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problems in Web mining.

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Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan Nanlu, Beijing, 100080, People's Republic of China

    Zhongzhi Shi, Huifang Ma & Qing He

Authors
  1. Zhongzhi Shi
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  2. Huifang Ma
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  3. Qing He
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Corresponding author

Correspondence to Zhongzhi Shi .

Editor information

Editors and Affiliations

  1. School of Software Faculty of Engineering and Information Technology, University of Technology, PO Box 123, Sydney, Broadway, NSW 2007, Australia

    Longbing Cao  & Huaifeng Zhang  & 

  2. Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan St., Chicago, IL, 60607

    Philip S. Yu

  3. Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology, University of Technology, PO Box 123, Sydney, Broadway, NSW 2007, Australia

    Chengqi Zhang

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Shi, Z., Ma, H., He, Q. (2009). Web Mining: Extracting Knowledge from the World Wide Web. In: Cao, L., Yu, P.S., Zhang, C., Zhang, H. (eds) Data Mining for Business Applications. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79420-4_14

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  • DOI: https://doi.org/10.1007/978-0-387-79420-4_14

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-79419-8

  • Online ISBN: 978-0-387-79420-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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