Abstract
Recent studies on first- and second-order similarities have shown that the latter one outperforms the first one as input for document clustering or partitioning applications. First-order similarities based on bibliographic coupling or on lexical approaches come with specific methodological issues like sparse matrices, sensitive to spelling variances or context differences. Second-order similarities were proposed to tackle these problems and take the lexical context into account. But also a hybrid combination of both types of similarities proved an important improvement which integrates the strengths of the two approaches and diminishes their weaknesses. In this paper we extend the notion of second-order similarity by applying it in the context of the hybrid approach. We conclude that there is no added value for the clearly defined clusters but that the second-order similarity can provide an additional viewpoint for the more general clusters.
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Acknowledgments
This is a version of a paper presented by the first author at the 17th International Conference on Science and Technology Indicators (Montreal, 5–8 September 2012). The full proceedings are published as: E. Archambault, Y. Gingras, V. Lariviere (Eds.), Proceedings of STI 2012 Montreal, Science-Metrix and OST, Montréal, Quebec, Canada.
The methodology has partially been developed in the context of the ERACEP project within the Coordination and Support Actions (CSAs) of the ERC work programme. The authors wish to acknowledge this support.
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Thijs, B., Schiebel, E. & Glänzel, W. Do second-order similarities provide added-value in a hybrid approach?. Scientometrics 96, 667–677 (2013). https://doi.org/10.1007/s11192-012-0896-1
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DOI: https://doi.org/10.1007/s11192-012-0896-1