Search (15 results, page 1 of 1)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Data Mining"
  1. Mandl, T.: Text mining und data minig (2013) 0.04
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    Source
    Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried
  2. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.03
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    Date
    22. 3.2013 19:43:01
  3. Mining text data (2012) 0.02
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    Editor
    Aggarwal, C.C. u. C.X. Zhai
    Footnote
    Elektronische Ausgabe unter: http://springer.r.delivery.net/r/r?2.1.Ee.2Tp.1gd0L5.C3WE8i..N.WdtI.3uq2.bW89MQ%5f%5fCXccFOL0.
  4. Kipcic, O.; Cramer, C.: Wie Zeitungsinhalte Forschung und Entwicklung befördern (2017) 0.02
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  5. Maaten, L. van den: Accelerating t-SNE using Tree-Based Algorithms (2014) 0.02
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    Abstract
    The paper investigates the acceleration of t-SNE-an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots-using two tree-based algorithms. In particular, the paper develops variants of the Barnes-Hut algorithm and of the dual-tree algorithm that approximate the gradient used for learning t-SNE embeddings in O(N*logN). Our experiments show that the resulting algorithms substantially accelerate t-SNE, and that they make it possible to learn embeddings of data sets with millions of objects. Somewhat counterintuitively, the Barnes-Hut variant of t-SNE appears to outperform the dual-tree variant.
  6. Leydesdorff, L.; Persson, O.: Mapping the geography of science : distribution patterns and networks of relations among cities and institutes (2010) 0.01
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  7. Chardonnens, A.; Hengchen, S.: Text mining for cultural heritage institutions : a 5-step method for cultural heritage institutions (2017) 0.01
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    Source
    Everything changes, everything stays the same? - Understanding information spaces : Proceedings of the 15th International Symposium of Information Science (ISI 2017), Berlin/Germany, 13th - 15th March 2017. Eds.: M. Gäde, V. Trkulja u. V. Petras
  8. Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010) 0.01
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    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
  9. Berry, M.W.; Esau, R.; Kiefer, B.: ¬The use of text mining techniques in electronic discovery for legal matters (2012) 0.01
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  10. Ebrahimi, M.; ShafieiBavani, E.; Wong, R.; Chen, F.: Twitter user geolocation by filtering of highly mentioned users (2018) 0.01
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  11. Wongthontham, P.; Abu-Salih, B.: Ontology-based approach for semantic data extraction from social big data : state-of-the-art and research directions (2018) 0.01
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  12. Liu, B.: Web data mining : exploring hyperlinks, contents, and usage data (2011) 0.01
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    Footnote
    Elektronische Ausgabe unter: http://springer.r.delivery.net/r/r?2.1.Ee.2Tp.1gd0L5.C3WE8i..N.WdtE.3uq2.bW89MQ%5f%5fCXPUFOH0.
  13. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  14. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.00
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    Date
    7. 3.2019 16:32:22
  15. Jäger, L.: Von Big Data zu Big Brother (2018) 0.00
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    Date
    22. 1.2018 11:33:49