Search (77 results, page 4 of 4)

  • × theme_ss:"Data Mining"
  1. Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013) 0.00 
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    Date
    25. 6.2013 19:05:29
  2. Tu, Y.-N.; Hsu, S.-L.: Constructing conceptual trajectory maps to trace the development of research fields (2016) 0.00 
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    Date
    21. 7.2016 19:29:19
  3. 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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    Date
    16.11.2017 14:00:29
  4. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.00 
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    Date
    22. 3.2013 19:43:01
  5. 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
  6. 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
  7. Zhou, L.; Chaovalit, P.: Ontology-supported polarity mining (2008) 0.00 
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  8. Srinivasan, P.: Text mining : generating hypotheses from MEDLINE (2004) 0.00 
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  9. 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.
  10. Li, J.; Zhang, P.; Cao, J.: External concept support for group support systems through Web mining (2009) 0.00 
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  11. Mohr, J.W.; Bogdanov, P.: Topic models : what they are and why they matter (2013) 0.00 
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  12. 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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  13. Wongthontham, P.; Abu-Salih, B.: Ontology-based approach for semantic data extraction from social big data : state-of-the-art and research directions (2018) 0.00 
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  14. Organisciak, P.; Schmidt, B.M.; Downie, J.S.: Giving shape to large digital libraries through exploratory data analysis (2022) 0.00 
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  15. Lowe, D.B.; Dollinger, I.; Koster, T.; Herbert, B.E.: Text mining for type of research classification (2021) 0.00 
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    Source
    Cataloging and classification quarterly. 59(2021) no.8, p.815-834
  16. Chen, C.-C.; Chen, A.-P.: Using data mining technology to provide a recommendation service in the digital library (2007) 0.00 
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  17. 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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