A principled methodology for comparing relatedness measures for clustering publications

Quantitative Science Studies, vol. 1, pp. 691–713

Abstract

Abstract There are many different relatedness measures, based for instance on citation relations or textual similarity, that can be used to cluster scientific publications. We propose a principled methodology for evaluating the accuracy of clustering solutions obtained using these relatedness measures. We formally show that the proposed methodology has an important consistency property. The empirical analyses that we present are based on publications in the fields of cell biology, condensed matter physics, and economics. Using the BM25 text-based relatedness measure as the evaluation criterion, we find that bibliographic coupling relations yield more accurate clustering solutions than direct citation relations and cocitation relations. The so-called extended direct citation approach performs similarly to or slightly better than bibliographic coupling in terms of the accuracy of the resulting clustering solutions. The other way around, using a citation-based relatedness measure as evaluation criterion, BM25 turns out to yield more accurate clustering solutions than other text-based relatedness measures.

Authors 4

1. Ludo Waltman corresponding Aachen Centre for Science and Technology Studies

   Leiden University

   Affiliation as printed

   Centre for Science and Technology Studies, Leiden University, The Netherlands

2. Kevin W. Boyack

   SciTech Strategies (United States)

   Affiliation as printed

   SciTech Strategies, Inc., Albuquerque, NM, USA

3. Giovanni Colavizza

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam, The Netherlands

   UNIVERSITY OF AMSTERDAM, THE NETHERLANDS

4. Nees Jan van Eck Aachen Centre for Science and Technology Studies

   Leiden University

   Affiliation as printed

   Centre for Science and Technology Studies, Leiden University, The Netherlands