 Document–document similarity approaches and science mapping: Experimental comparison of five approachesPer Ahlgren and Cristian Colliander Journal of Informetrics, 2009, vol. 3, issue 1, 49-63 Abstract: This paper treats document–document similarity approaches in the context of science mapping. Five approaches, involving nine methods, are compared experimentally. We compare text-based approaches, the citation-based bibliographic coupling approach, and approaches that combine text-based approaches and bibliographic coupling. Forty-three articles, published in the journal Information Retrieval, are used as test documents. We investigate how well the approaches agree with a ground truth subject classification of the test documents, when the complete linkage method is used, and under two types of similarities, first-order and second-order. The results show that it is possible to achieve a very good approximation of the classification by means of automatic grouping of articles. One text-only method and one combination method, under second-order similarities in both cases, give rise to cluster solutions that to a large extent agree with the classification. Keywords: Citation data; Textual data; Data source combination; Cluster analysis; Science mapping (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:3:y:2009:i:1:p:49-63 DOI: 10.1016/j.joi.2008.11.003 Access Statistics for this article Journal of Informetrics is currently edited by Leo Egghe More articles in Journal of Informetrics from Elsevier |
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