Document (#35465)

Author

Liu, X.
Yu, S.
Janssens, F.
Glänzel, W.
Moreau, Y.
Moor, B.de

Title

Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119

Year

2010

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.

Theme

Automatisches Klassifizieren
Data Mining

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