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- rvk_ss%3a%2200 80852 allgemeines %2f buch- und bibliothekswesen%2c informationswissenschaft %2f bibliothekswesen %2f bibliothekswesen in einzelnen L%c3%aendern und einzelne bibliotheken %2f einzelne deutsche bibliotheken %2f bibliotheken S %2f stuttgart %2f university%c3%A4tsbibliothek hohenheim%22 2
- rvk_ss%3a%2200 80852 allgemeines %2f buch- und bibliothekswesen%2c informationswissenschaft %2f bibliothekswesen %2f bibliothekswesen in einzelnen L%c3%aendern und einzelne bibliotheken %2f einzelne deutsche bibliotheken %2f bibliotheken S %2f stuttgart %2f université%c3%A4tsbibliothek hohenheim%22 2
- rvk_ss%3a%2200 80852 allgemeines %2f buch- und bibliothekswesen%2c informationswissenschaft %2f bibliothekswesen %2f bibliothekswesen in einzelnen L%c3%aendern und einzelne bibliotheken %2f einzelnen deutsche bibliotheken %2f bibliotheken S %2f stuttgart %2f university%c3%A4tsbibliothek hohenheim%22 2
- rvk_ss%3a%2200 80852 allgemeines %2f buch- und bibliothekswesen%2c informationswissenschaft %2f bibliothekswesen %2f bibliothekswesen in einzelnen L%c3%aendern und einzelnen bibliotheken %2f einzelne deutsche bibliotheken %2f bibliotheken S %2f stuttgart %2f university%c3%A4tsbibliothek hohenheim%22 2
- rvk_ss%3a%2200 80852 allgemeines %2f buch- und bibliothekswesen%2c informationswissenschaft %2f bibliothekswesen %2f bibliothekswesen in einzelnen L%c3%andern und einzelne bibliotheken %2f einzelne deutsche bibliotheken %2f bibliotheken S %2f stuttgart %2f university%c3%A4tsbibliothek hohenheim%22 2
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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)
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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.
- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119
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Chen, S.Y.; Liu, X.: ¬The contribution of data mining to information science : making sense of it all (2005)
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- Source
- Journal of information science. 30(2005) no.6, S.550-
Authors
- Chen, S.Y. 1
- Glänzel, W. 1
- Janssens, F. 1
- Moor, B.de 1
- Moreau, Y. 1
- Yu, S. 1