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A Systematic Evaluation of Clustering Algorithms Against Expert-Derived Clustering

Author

Listed:
  • Ioannis Mikrou

    (University of Western Macedonia, ZEP)

  • Nickolas S. Sapidis

    (University of Western Macedonia, ZEP
    Hellenic Open University)

Abstract

Clustering algorithms have long sought to replicate human expertise in data clustering. This research utilizes six mechatronic products and a team of domain experts to assess how closely the results of six clustering algorithms align with expert-generated outcomes. This comparison of clustering results includes validation indices, the analysis of component migrations between the generated clusters, and optical inspection. The mechatronic products were selected for their complex nature, which provides a challenging context for the clustering analysis. The algorithms used in this research are Ward’s method (WARD), partitioning around medoids (PAM), K-means (K-MEANS), divisive analysis (DIANA), density-based spatial clustering of applications with noise (DBSCAN), and clustering by fast search and find of density peaks (DPC), and the validation indices employed to evaluate the clustering results are the silhouette coefficient (SC) and the composed density between and within clusters (CDbw).

Suggested Citation

  • Ioannis Mikrou & Nickolas S. Sapidis, 2025. "A Systematic Evaluation of Clustering Algorithms Against Expert-Derived Clustering," SN Operations Research Forum, Springer, vol. 6(2), pages 1-24, June.
    • Handle: RePEc:spr:snopef:v:6:y:2025:i:2:d:10.1007_s43069-025-00453-w
    DOI: 10.1007/s43069-025-00453-w
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    References listed on IDEAS

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    1. Ioannis Mikrou & Nickolas S. Sapidis, 2024. "Enhancing operational research in mechatronic systems via modularization: comparative analysis of four clustering algorithms using validation indices," Operational Research, Springer, vol. 24(4), pages 1-44, December.
    2. Yeow Chong Goh & Xin Qing Cai & Walter Theseira & Giovanni Ko & Khiam Aik Khor, 2020. "Evaluating human versus machine learning performance in classifying research abstracts," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1197-1212, November.
    Full references (including those not matched with items on IDEAS)

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