Search (46 results, page 1 of 3)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Data Mining"
  1. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.02 
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    Date
    22. 3.2013 19:43:01
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.574-586
  2. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.02 
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    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  3. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.02 
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    Date
    7. 3.2019 16:32:22
    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.4, S.402-411
  4. Jäger, L.: Von Big Data zu Big Brother (2018) 0.01 
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    Date
    22. 1.2018 11:33:49
  5. Blake, C.: Text mining (2011) 0.00 
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    Source
    Annual review of information science and technology. 45(2011) no.1, S.121-155
  6. Chardonnens, A.; Hengchen, S.: Text mining for cultural heritage institutions : a 5-step method for cultural heritage institutions (2017) 0.00 
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    Pages
    S.177-191
  7. Berendt, B.; Krause, B.; Kolbe-Nusser, S.: Intelligent scientific authoring tools : interactive data mining for constructive uses of citation networks (2010) 0.00 
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    Abstract
    Many powerful methods and tools exist for extracting meaning from scientific publications, their texts, and their citation links. However, existing proposals often neglect a fundamental aspect of learning: that understanding and learning require an active and constructive exploration of a domain. In this paper, we describe a new method and a tool that use data mining and interactivity to turn the typical search and retrieve dialogue, in which the user asks questions and a system gives answers, into a dialogue that also involves sense-making, in which the user has to become active by constructing a bibliography and a domain model of the search term(s). This model starts from an automatically generated and annotated clustering solution that is iteratively modified by users. The tool is part of an integrated authoring system covering all phases from search through reading and sense-making to writing. Two evaluation studies demonstrate the usability of this interactive and constructive approach, and they show that clusters and groups represent identifiable sub-topics.
    Source
    Information processing and management. 46(2010) no.1, S.1-10
  8. Mandl, T.: Text mining und data minig (2013) 0.00 
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    Pages
    S.183-191
  9. 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
  10. Gill, A.J.; Hinrichs-Krapels, S.; Blanke, T.; Grant, J.; Hedges, M.; Tanner, S.: Insight workflow : systematically combining human and computational methods to explore textual data (2017) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.7, S.1671-1686
  11. Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119
  12. Sarnikar, S.; Zhang, Z.; Zhao, J.L.: Query-performance prediction for effective query routing in domain-specific repositories (2014) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.8, S.1597-1614
  13. Bella, A. La; Fronzetti Colladon, A.; Battistoni, E.; Castellan, S.; Francucci, M.: Assessing perceived organizational leadership styles through twitter text mining (2018) 0.00 
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    Journal of the Association for Information Science and Technology. 69(2018) no.1, S.21-31
  14. Teich, E.; Degaetano-Ortlieb, S.; Fankhauser, P.; Kermes, H.; Lapshinova-Koltunski, E.: ¬The linguistic construal of disciplinarity : a data-mining approach using register features (2016) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1668-1678
  15. Derek Doran, D.; Gokhale, S.S.: ¬A classification framework for web robots (2012) 0.00 
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    Journal of the American Society for Information Science and Technology. 63(2012) no.12, S.2549-2554,
  16. Wei, C.-P.; Lee, Y.-H.; Chiang, Y.-S.; Chen, C.-T.; Yang, C.C.C.: Exploiting temporal characteristics of features for effectively discovering event episodes from news corpora (2014) 0.00 
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    Journal of the Association for Information Science and Technology. 65(2014) no.3, S.621-634
  17. Ekbia, H.; Mattioli, M.; Kouper, I.; Arave, G.; Ghazinejad, A.; Bowman, T.; Suri, V.R.; Tsou, A.; Weingart, S.; Sugimoto, C.R.: Big data, bigger dilemmas : a critical review (2015) 0.00 
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    Journal of the Association for Information Science and Technology. 66(2015) no.8, S.1523-1545
  18. Tu, Y.-N.; Hsu, S.-L.: Constructing conceptual trajectory maps to trace the development of research fields (2016) 0.00 
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    Journal of the Association for Information Science and Technology. 67(2016) no.8, S.2016-2031
  19. Tonkin, E.L.; Tourte, G.J.L.: Working with text. tools, techniques and approaches for text mining (2016) 0.00 
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    Footnote
    Rez. in: JASIST 69(2018) no.1, S.181-184 (Jacques Savoy).
    Pages
    xiii, 330 S
  20. Saggi, M.K.; Jain, S.: ¬A survey towards an integration of big data analytics to big insights for value-creation (2018) 0.00 
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    Source
    Information processing and management. 54(2018) no.5, S.758-790

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