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June 13, 2026SoftwareXOpen Access

ClusterChoice: a web-based tool for interpretable clustering and algorithm comparison in bibliometric networks

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Authors

TCTsair‐Wei ChienLCL M ChenTCTsair‐Wei Chien

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Overview

Randomized trial compares clustering algorithms in bibliometric networks, suggesting improved selection with ClusterChoice.

Key Points

  • This research aims to improve the selection and interpretation of clustering structures in bibliometric networks using a web-based tool.
  • Developed a web-based R Shiny application named ClusterChoice to analyze bibliometric networks.
  • Compared 11 clustering algorithms using metrics like modularity, silhouette score, ARI, and NMI.
  • Conducted analyses on a SoftwareX bibliographic dataset to demonstrate practical utility.
  • FLCA achieved the highest modularity (Q = 0.80) and mean silhouette score (0.78), indicating better clustering quality.
  • Conventional algorithms displayed similar modularity (Q = 0.65) but near-zero silhouette scores, reflecting poor cohesion.
  • Components-based methods improved silhouette scores to 0.60–0.62, enhancing clarity of clusters.

Cite This Study

Chien et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf488faef96ed7f056c4chttps://doi.org/10.1016/j.softx.2026.102794
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