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Web Mining

In: Machine Learning for Data Science Handbook

Author

Listed:
  • Petar Ristoski

    (IBM Research)

Abstract

The World Wide Web allows users and organizations to publish information and documents, which are instantly available for all other users of the Web. The data published to the Web continuously increases, providing the users with a vast amount of information on any topic imaginable. However, navigating the Web and identifying the relevant pieces of information in the abundance of data is not trivial. To cope with this problem, Web mining approaches are being used. Web mining includes the application of information retrieval, data mining, and machine learning approaches on Web data and the Web structure. This chapter provides a brief summary of Web mining approaches, including Web content mining, Web structure mining, Web usage mining, and Semantic Web mining.

Suggested Citation

  • Petar Ristoski, 2023. "Web Mining," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 447-467, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-24628-9_20
    DOI: 10.1007/978-3-031-24628-9_20
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