Search (6 results, page 1 of 1)

  • × theme_ss:"Data Mining"
  • × theme_ss:"Internet"
  • × type_ss:"a"
  1. Fenstermacher, K.D.; Ginsburg, M.: Client-side monitoring for Web mining (2003) 0.01
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    Footnote
    Teil eines Themenheftes: "Web retrieval and mining: A machine learning perspective"
    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.7, S.625-637
  2. Raan, A.F.J. van; Noyons, E.C.M.: Discovery of patterns of scientific and technological development and knowledge transfer (2002) 0.00
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    Source
    Gaining insight from research information (CRIS2002): Proceedings of the 6th International Conference an Current Research Information Systems, University of Kassel, August 29 - 31, 2002. Eds: W. Adamczak u. A. Nase
  3. Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010) 0.00
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    Abstract
    This paper discusses an approach of collecting qualitative data on human information behaviour that is based on mining web data using search engines. The approach is technically the same that has been used for some time in webometric research to make statistical inferences on web data, but the present paper shows how the same tools and data collecting methods can be used to gather data for qualitative data analysis on human information behaviour.
    Source
    Information und Wissen: global, sozial und frei? Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) ; Hildesheim, 9. - 11. März 2011. Hrsg.: J. Griesbaum, T. Mandl u. C. Womser-Hacker
  4. Chen, H.; Chau, M.: Web mining : machine learning for Web applications (2003) 0.00
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    Abstract
    With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. Analysis of these characteristics often reveals interesting patterns and new knowledge. Such knowledge can be used to improve users' efficiency and effectiveness in searching for information an the Web, and also for applications unrelated to the Web, such as support for decision making or business management. The Web's size and its unstructured and dynamic content, as well as its multilingual nature, make the extraction of useful knowledge a challenging research problem. Furthermore, the Web generates a large amount of data in other formats that contain valuable information. For example, Web server logs' information about user access patterns can be used for information personalization or improving Web page design.
    Source
    Annual review of information science and technology. 38(2004), S.289-330
  5. Derek Doran, D.; Gokhale, S.S.: ¬A classification framework for web robots (2012) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.12, S.2549-2554,
  6. Kong, S.; Ye, F.; Feng, L.; Zhao, Z.: Towards the prediction problems of bursting hashtags on Twitter (2015) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2566-2579