Search (25 results, page 1 of 2)

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  • × theme_ss:"Data Mining"
  1. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.03
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    Date
    7. 3.2019 16:32:22
  2. 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
  3. 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
  4. 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
  5. Sperlich, T.: ¬Die Zukunft hat schon begonnen : Visualisierungssoftware in der praktischen Anwendung (2000) 0.01
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    Content
    1. Unsichtbares sichtbar machen - 2. Mit 3D-Darsteltungen besser verkaufen - 3. Mixed Realities - 4. Informationstechnik hilft heilen - 5. Informationen finden - Komplexes verstehen - 6. Informationslandschaften - Karten - 7. Arbeiten und Wohnen in der Info-Zukunft - 8. Neues Lernen in der Info-Welt - 9. Computerspiele alsTechnologie-Avantgarde - 10. Multimediale Kunst
  6. 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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    Date
    7. 4.2017 19:18:05
  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. Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013) 0.01
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    Abstract
    Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1399-1410
  9. Wong, S.K.M.; Butz, C.J.; Xiang, X.: Automated database schema design using mined data dependencies (1998) 0.01
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    Date
    7. 2.1999 11:16:36
  10. Raghavan, V.V.; Deogun, J.S.; Sever, H.: Knowledge discovery and data mining : introduction (1998) 0.01
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    Date
    7. 2.1999 11:23:06
  11. Mandl, T.: Text Mining und Data Mining (2023) 0.01
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    Source
    Grundlagen der Informationswissenschaft. Hrsg.: Rainer Kuhlen, Dirk Lewandowski, Wolfgang Semar und Christa Womser-Hacker. 7., völlig neu gefasste Ausg
  12. Fenstermacher, K.D.; Ginsburg, M.: Client-side monitoring for Web mining (2003) 0.01
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    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.7, S.625-637
  13. Chen, Y.-L.; Liu, Y.-H.; Ho, W.-L.: ¬A text mining approach to assist the general public in the retrieval of legal documents (2013) 0.01
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    Date
    7. 2.2013 19:25:40
  14. Sun, X.; Lin, H.: Topical community detection from mining user tagging behavior and interest (2013) 0.01
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    Date
    7. 2.2013 19:31:28
  15. 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) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1668-1678
  16. Ebrahimi, M.; ShafieiBavani, E.; Wong, R.; Chen, F.: Twitter user geolocation by filtering of highly mentioned users (2018) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 69(2018) no.7, S.879-889
  17. Klein, H.: Web Content Mining (2004) 0.01
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    Series
    Fortschritte in der Wissensorganisation; Bd.7
    Source
    Wissensorganisation und Edutainment: Wissen im Spannungsfeld von Gesellschaft, Gestaltung und Industrie. Proceedings der 7. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Berlin, 21.-23.3.2001. Hrsg.: C. Lehner, H.P. Ohly u. G. Rahmstorf
  18. 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
  19. 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
  20. 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) 0.01
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    Date
    7. 7.2015 20:01:21