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  • × theme_ss:"Automatisches Klassifizieren"
  • × theme_ss:"Data Mining"
  • × type_ss:"a"
  1. 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) 0.01 
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    Abstract
    We propose a new hybrid clustering framework to incorporate text mining with bibliometrics in journal set analysis. The framework integrates two different approaches: clustering ensemble and kernel-fusion clustering. To improve the flexibility and the efficiency of processing large-scale data, we propose an information-based weighting scheme to leverage the effect of multiple data sources in hybrid clustering. Three different algorithms are extended by the proposed weighting scheme and they are employed on a large journal set retrieved from the Web of Science (WoS) database. The clustering performance of the proposed algorithms is systematically evaluated using multiple evaluation methods, and they were cross-compared with alternative methods. Experimental results demonstrate that the proposed weighted hybrid clustering strategy is superior to other methods in clustering performance and efficiency. The proposed approach also provides a more refined structural mapping of journal sets, which is useful for monitoring and detecting new trends in different scientific fields.
    Date
    1. 6.2010 9:29:57
  2. Fong, A.C.M.: Mining a Web citation database for document clustering (2002) 0.00 
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  3. 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

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