Bibliographic coupling and hierarchical clustering for the validation and improvement of subject-classification schemes
ScientometricsPublished 22 July 2015
Bart Thijs, Lin Zhang, Wolfgang Glänzel
Citations34
SJR quartileQ1
SJR score1.15
SNIP1.82
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TL;DR
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.
Abstract
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.
Keywords
Decision Sciences
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