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A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric

Teng-Yun Cheng1, Sam Yu-Chieh Ho2, Tsair-Wei Chien3

  • 1Department of Emergency Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan.

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Summary
This summary is machine-generated.

A new Following Leader Clustering Algorithm (FLCA) improves bibliometric analysis by effectively identifying author collaborations and research themes. This method reveals significant differences in clustering outcomes with and without self-connections, enhancing research discovery.

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Area of Science:

  • Bibliometrics and scientometrics
  • Information science
  • Network analysis

Background:

  • Bibliometrics faces challenges in defining author collaborations, developing effective clustering algorithms for coword analysis, and comparing research achievements.
  • Existing bibliometric methods inadequately support identifying impactful research and recommending relevant articles to readers.

Purpose of the Study:

  • To introduce the Following Leader Clustering Algorithm (FLCA) for cluster analysis.
  • To investigate differences in cluster analysis outcomes with and without self-connections.
  • To demonstrate the application of FLCA in bibliometrics for analyzing author collaborations and research themes.

Main Methods:

  • Searched JMIR Medical Informatics articles (2016-2022) from Web of Science core collections.
  • Applied the FLCA algorithm to identify author collaborations (ACs) and themes.
  • Compared cluster results with and without self-connections, using traditional bibliometric counts and citations, with R for visualization.

Main Results:

  • A significant difference in cluster outcomes was observed between analyses with and without self-connections (53.8% overlap).
  • Yonsei University (South Korea), Grang Luo (US), and specific institutes led top clusters in author collaborations and themes.
  • The United States, Yonsei University, and Grang Luo were identified as top entities in JMIR Medical Informatics publications.

Conclusions:

  • The FLCA algorithm provides a robust method for researchers to understand complex author and keyword relationships.
  • FLCA and R visualizations are recommended for future bibliometric studies involving cluster analysis of author collaborations.