Search (30 results, page 1 of 2)

  • × language_ss:"e"
  • × theme_ss:"Data Mining"
  1. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.02
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    Date
    2. 4.2000 18:01:22
  2. KDD : techniques and applications (1998) 0.02
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    Footnote
    A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997
  3. Fong, A.C.M.: Mining a Web citation database for document clustering (2002) 0.01
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    Date
    27. 8.2005 13:20:47
  4. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.01
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    Date
    22.11.1998 18:57:22
  5. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.01
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    Source
    Information systems. 22(1997) nos.5/6, S.333-347
  6. Cios, K.J.; Pedrycz, W.; Swiniarksi, R.: Data mining methods for knowledge discovery (1998) 0.01
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    Date
    16. 8.2002 13:20:27
  7. Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.01
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    Source
    Information systems. 22(1997) nos.5/6, S.349-385
  8. Fayyad, U.M.: Data mining and knowledge dicovery : making sense out of data (1996) 0.01
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    Source
    IEEE expert. 11(1996) no.5, S.20-25
  9. Borgman, C.L.; Wofford, M.F.; Golshan, M.S.; Darch, P.T.: Collaborative qualitative research at scale : reflections on 20 years of acquiring global data and making data global (2021) 0.01
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    Abstract
    A 5-year project to study scientific data uses in geography, starting in 1999, evolved into 20 years of research on data practices in sensor networks, environmental sciences, biology, seismology, undersea science, biomedicine, astronomy, and other fields. By emulating the "team science" approaches of the scientists studied, the UCLA Center for Knowledge Infrastructures accumulated a comprehensive collection of qualitative data about how scientists generate, manage, use, and reuse data across domains. Building upon Paul N. Edwards's model of "making global data"-collecting signals via consistent methods, technologies, and policies-to "make data global"-comparing and integrating those data, the research team has managed and exploited these data as a collaborative resource. This article reflects on the social, technical, organizational, economic, and policy challenges the team has encountered in creating new knowledge from data old and new. We reflect on continuity over generations of students and staff, transitions between grants, transfer of legacy data between software tools, research methods, and the role of professional data managers in the social sciences.
    Date
    20. 5.2021 18:35:59
  10. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.01
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    Date
    22. 3.2013 19:43:01
  11. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  12. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.01
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    Date
    7. 3.2019 16:32:22
  13. Search tools (1997) 0.01
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    Date
    26. 5.1996 20:34:13
  14. Galal, G.M.; Cook, D.J.; Holder, L.B.: Exploiting parallelism in a structural scientific discovery system to improve scalability (1999) 0.01
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    Date
    19. 2.1999 20:00:06
  15. Fenstermacher, K.D.; Ginsburg, M.: Client-side monitoring for Web mining (2003) 0.01
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    Date
    17. 5.2003 20:09:11
  16. Loh, S.; Oliveira, J.P.M. de; Gastal, F.L.: Knowledge discovery in textual documentation : qualitative and quantitative analyses (2001) 0.01
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    Date
    10. 3.1997 20:31:13
  17. Gaizauskas, R.; Wilks, Y.: Information extraction : beyond document retrieval (1998) 0.01
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    Date
    10. 3.1997 20:31:13
  18. Li, J.; Zhang, P.; Cao, J.: External concept support for group support systems through Web mining (2009) 0.01
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    Date
    18. 4.2009 20:59:21
  19. Thelwall, M.; Wilkinson, D.; Uppal, S.: Data mining emotion in social network communication : gender differences in MySpace (2009) 0.01
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    Abstract
    Despite the rapid growth in social network sites and in data mining for emotion (sentiment analysis), little research has tied the two together, and none has had social science goals. This article examines the extent to which emotion is present in MySpace comments, using a combination of data mining and content analysis, and exploring age and gender. A random sample of 819 public comments to or from U.S. users was manually classified for strength of positive and negative emotion. Two thirds of the comments expressed positive emotion, but a minority (20%) contained negative emotion, confirming that MySpace is an extraordinarily emotion-rich environment. Females are likely to give and receive more positive comments than are males, but there is no difference for negative comments. It is thus possible that females are more successful social network site users partly because of their greater ability to textually harness positive affect.
  20. Frické, M.: Big data and its epistemology (2015) 0.01
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    Date
    20. 3.2015 18:23:25

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