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kmed: An R package for reproducible distance-based k-medoids clustering

Weksi Budiaji & Ferdian Bangkit Wijaya

Software Impacts2026https://doi.org/10.1016/j.simpa.2026.100833article
AJG 1
Weight
0.50

What the paper says

k-medoids clustering provides a robust alternative to k-means when data contain outliers or non-Euclidean dissimilarities. We introduce kmed, an open-source R package providing a reproducible pipeline for various distance formulations, k-medoids variants, validation techniques, and visualizations. The package emphasizes flexibility and ease of use for researchers integrating k-medoids into reproducible research workflows. kmed implements established, peer-reviewed algorithms widely applied across statistics and machine learning. By providing a standardized R framework, kmed facilitates reuse and promotes reproducibility in clustering-based research, supporting both exploratory and confirmatory data analysis. • Provides a reproducible R framework for distance-based k-medoids clustering. • Supports numerical, categorical, and mixed-type distances in a unified workflow. • Integrates clustering, validation, and visualization in open-source software. • Enables transparent and reusable clustering analyses across scientific domains.

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https://doi.org/https://doi.org/10.1016/j.simpa.2026.100833

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@article{weksi2026,
  title        = {{kmed: An R package for reproducible distance-based k-medoids clustering}},
  author       = {Weksi Budiaji & Ferdian Bangkit Wijaya},
  journal      = {Software Impacts},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.simpa.2026.100833},
}

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kmed: An R package for reproducible distance-based k-medoids clustering

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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