Search (52 results, page 1 of 3)

  • × language_ss:"e"
  • × theme_ss:"Data Mining"
  • × year_i:[2000 TO 2010}
  1. Information visualization in data mining and knowledge discovery (2002) 0.01 
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    Date
    23. 3.2008 19:10:22
    Language
    e
  2. Budzik, J.; Hammond, K.J.; Birnbaum, L.: Information access in context (2001) 0.00 
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  3. Fong, A.C.M.: Mining a Web citation database for document clustering (2002) 0.00 
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  4. Intelligent information processing and web mining : Proceedings of the International IIS: IIPWM'03 Conference held in Zakopane, Poland, June 2-5, 2003 (2003) 0.00 
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  5. Chen, S.Y.; Liu, X.: ¬The contribution of data mining to information science : making sense of it all (2005) 0.00 
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  6. Liu, W.; Weichselbraun, A.; Scharl, A.; Chang, E.: Semi-automatic ontology extension using spreading activation (2005) 0.00 
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  7. Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004) 0.00 
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    Abstract
    Topic discovery is an important means for marketing, e-Business and social science studies. As well, it can be applied to various purposes, such as identifying a group with certain properties and observing the emergence and diminishment of a certain cyber community. Previous topic discovery work (J.M. Kleinberg, Proceedings of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms, San Francisco, California, p. 668) requires manual judgment of usefulness of outcomes and is thus incapable of handling the explosive growth of the Internet. In this paper, we propose the Automatic Topic Discovery (ATD) method, which combines a method of base set construction, a clustering algorithm and an iterative principal eigenvector computation method to discover the topics relevant to a given query without using manual examination. Given a query, ATD returns with topics associated with the query and top representative pages for each topic. Our experiments show that the ATD method performs better than the traditional eigenvector method in terms of computation time and topic discovery quality.
    Language
    e
  8. Dang, X.H.; Ong. K.-L.: Knowledge discovery in data streams (2009) 0.00 
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    Footnote
    Vgl.: http://www.tandfonline.com/doi/book/10.1081/E-ELIS3.
    Language
    e
  9. Bath, P.A.: Data mining in health and medical information (2003) 0.00 
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  10. Knowledge management in fuzzy databases (2000) 0.00 
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  11. Lam, W.; Yang, C.C.; Menczer, F.: Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007) 0.00 
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  12. Baeza-Yates, R.; Hurtado, C.; Mendoza, M.: Improving search engines by query clustering (2007) 0.00 
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  13. Zhou, L.; Chaovalit, P.: Ontology-supported polarity mining (2008) 0.00 
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  14. Maaten, L. van den: Learning a parametric embedding by preserving local structure (2009) 0.00 
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  15. Kantardzic, M.: Data mining : concepts, models, methods, and algorithms (2003) 0.00 
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    Classification
    TWX (E)
    GHBS
    TWX (E)
    Language
    e
  16. Wong, M.L.; Leung, K.S.; Cheng, J.C.Y.: Discovering knowledge from noisy databases using genetic programming (2000) 0.00 
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  17. Relational data mining (2001) 0.00 
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  18. Fenstermacher, K.D.; Ginsburg, M.: Client-side monitoring for Web mining (2003) 0.00 
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  19. Srinivasan, P.: Text mining : generating hypotheses from MEDLINE (2004) 0.00 
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  20. Ohly, H.P.: Bibliometric mining : added value from document analysis and retrieval (2008) 0.00 
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