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Bibliographic coupling and hierarchical clustering for the validation and improvement of subject-classification schemes

Author

Listed:
  • Bart Thijs

    (KU Leuven)

  • Lin Zhang

    (KU Leuven
    North China University of Water Conservancy and Electric Power)

  • Wolfgang Glänzel

    (KU Leuven
    Library of the Hungarian Academy of Sciences)

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.

Suggested Citation

  • Bart Thijs & Lin Zhang & Wolfgang Glänzel, 2015. "Bibliographic coupling and hierarchical clustering for the validation and improvement of subject-classification schemes," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1453-1467, December.
    • Handle: RePEc:spr:scient:v:105:y:2015:i:3:d:10.1007_s11192-015-1641-3
    DOI: 10.1007/s11192-015-1641-3
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    References listed on IDEAS

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    1. Lin Zhang & Frizo Janssens & Liming Liang & Wolfgang Glänzel, 2010. "Journal cross-citation analysis for validation and improvement of journal-based subject classification in bibliometric research," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(3), pages 687-706, March.
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    4. Bart Thijs & Edgar Schiebel & Wolfgang Glänzel, 2013. "Do second-order similarities provide added-value in a hybrid approach?," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 667-677, September.
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    7. Wolfgang Glänzel & Bart Thijs, 2011. "Using ‘core documents’ for the representation of clusters and topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 297-309, July.
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    Cited by:

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    4. Gerson Pech & Catarina Delgado & Silvio Paolo Sorella, 2022. "Classifying papers into subfields using Abstracts, Titles, Keywords and KeyWords Plus through pattern detection and optimization procedures: An application in Physics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(11), pages 1513-1528, November.
    5. Yu-Wei Chang, 2019. "Are articles in library and information science (LIS) journals primarily contributed to by LIS authors?," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 81-104, October.
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    7. Wuestman, Mignon L. & Hoekman, Jarno & Frenken, Koen, 2019. "The geography of scientific citations," Research Policy, Elsevier, vol. 48(7), pages 1771-1780.
    8. Lin Zhang & Beibei Sun & Fei Shu & Ying Huang, 2022. "Comparing paper level classifications across different methods and systems: an investigation of Nature publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7633-7651, December.
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