Abstract
An attempt is made to cluster journals from the complete Web of Science database by using bibliographic coupling similarities. Since the sparseness of the underlying similarity matrix proved inappropriate for this exercise, second-order similarities have been used. Only 0.12 % out of 8282 journals had to be removed from the classification as being singletons. The quality at three hierarchical levels with 6, 14 and 24 clusters substantiated the applicability of this method. Cluster labelling was made on the basis of the about 70 subfields of the Leuven–Budapest subject-classification scheme that also allowed the comparison with the existing two-level journal classification system developed in Leuven. The further comparison with the 22 field classification system of the Essential Science Indicators does, however, reveal larger deviations.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ahlgren, P., & Colliander, C. (2009). Document–document similarity approaches and science mapping: experimental comparison of five approaches. Journal of Informetrics, 3(1), 49–63.
Bassecoulard, E., & Zitt, M. (1999). Indicators in a research institute: A multi-level classification of scientific journals. Scientometrics, 44(3), 323–345.
Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. In International AAAI Conference on Weblogs and Social Media.
Garfield, E. (1998). Mapping the world of science. The 150 Anniversary Meeting of the AAAS, Philadelphia, PA, http://www.garfield.library.upenn.edu/papers/mapsciworld.html.
Glänzel, W., & Czerwon, H. J. (1996). A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level. Scientometrics, 37(2), 195–221.
Glänzel, W., & Schubert, A. (2003). A new classification scheme of science fields and subfields designed for scientometric evaluation purposes. Scientometrics, 56(3), 357–367.
Glänzel, W., & Thijs, B. (2011). Using ‘core documents’ for the representation of clusters and topics. Scientometrics, 88(1), 297–309.
Janssens, F. (2007). Clustering of scientific fields by integrating text mining and bibliometrics. Ph.D. Thesis, Faculty of Engineering, Belgium: KU Leuven. http://www.hdl.handle.net/1979/847.
Janssens, F., Glänzel, W., & de Moor, B. (2008). A hybrid mapping of information science. Scientometrics, 75(3), 607–631.
Janssens, F., Zhang, L., De Moor, B., & Glänzel, W. (2009). Hybrid clustering for validation and improvement of subject-classification schemes. Information Processing and Management, 45(6), 683–702.
Jarneving, B. (2005). The combined application of bibliographic coupling and the complete link cluster method in bibliometric science mapping. Ph.D. Thesis, University College of Borås/Göteborg University, Sweden.
Leydesdorff, L. (2004). Clusters and maps of science journals based on bi-connected graphs in Journal Citation Reports. Journal of Documentation, 60(4), 371–427.
Narin, F. (1976). Evaluative bibliometrics: The use of publication and citation analysis in the evaluation of scientific activity. Washington, DC: Computer Horizons Inc.
Narin, F., Carpenter, M., & Nancy, C. (1972). Interrelationships of scientific journals. Journal of the American Society for Information Science, 23(5), 323–331.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics, 20, 53–65.
Sen, S. K., & Gan, S. K. (1983). A mathematical extension of the idea of bibliographic coupling and its applications. Annals of Library Science and Documentation, 30, 78–82.
Thijs, B., Schiebel, E., & Glänzel, W. (2013a). Do second-order similarities provide added-value in a hybrid approach? Scientometrics, 96(3), 667–677.
Thijs, B., Zhang, L., & Glänzel, W. (2013b). Bibliographic coupling and hierarchical clustering for the validation and improvement of subject-classification schemes. In J. Gorraiz, E. Schiebel, Ch. Gumpenberger, M. Hörlesberger, H.F. Moed (Eds.), Proceedings of ISSI 2013: The 14th international conference on scientometrics and informetrics (vol. I, pp. 237–249), Vienna.
Zhang, L., Janssens, F., Liang, L. M., & Glänzel, W. (2010). Journal cross-citation analysis for validation and improvement of journal-based subject classification in bibliometric research. Scientometrics, 82(3), 687–706.
Acknowledgments
This is a revised and extended version of a paper presented at the 14th International Conference on Scientometrics and Informetrics, Vienna, Austria, 15–19 July 2013 (Thijs et al. 2013b). The authors wish to thank the reviewers for their comments which helped us to improve and extend the paper. Lin Zhang acknowledges the support from the National Natural Science Foundation of China under Grant 71103064.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
See Table 5.
Rights and permissions
About this article
Cite this article
Thijs, B., Zhang, L. & Glänzel, W. Bibliographic coupling and hierarchical clustering for the validation and improvement of subject-classification schemes. Scientometrics 105, 1453–1467 (2015). https://doi.org/10.1007/s11192-015-1641-3
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-015-1641-3