An R package for silhouette-based diagnostics in standard, soft, and multi-way clustering.
Silhouette diagnostics assess clustering quality using cohesion and separation of clusters. This package implements silhouette widths for various clustering setups, including support for soft membership probabilities and multi-way clustering structures.
You can install the released version of Silhouette from
GitHub using:
# Install devtools if needed
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
# Install from GitHub
devtools::install_github("kskbhat/Silhouette")Once accepted on CRAN, install via:
install.packages("Silhouette")Usage of the main functions is demonstrated in the package examples and documentation.
For an intro, see the vignette A quick tour of
Silhouette, which is available as
vignette("Silhouette")You can access the vignette from the User Guide tab in
the top navigation bar of the package’s website.
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Van der Laan, M., Pollard, K., & Bryan, J. (2003). A new partitioning around medoids algorithm. Journal of Statistical Computation and Simulation, 73(8), 575–584. doi:10.1080/0094965031000136012
Campello, R. J., & Hruschka, E. R. (2006). A fuzzy extension of the silhouette width criterion for cluster analysis. Fuzzy Sets and Systems, 157(21), 2858–2875. doi:10.1016/j.fss.2006.07.006
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Raymaekers, J., & Rousseeuw, P. J. (2022). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. Journal of Computational and Graphical Statistics, 31(4), 1332–1343. doi:10.1080/10618600.2022.2050249
Bhat Kapu, S., & Kiruthika. (2024). Some density-based silhouette diagnostics for soft clustering algorithms. Communications in Statistics: Case Studies, Data Analysis and Applications, 10(3–4), 221–238. doi:10.1080/23737484.2024.2408534
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., & Hornik, K. (2025). cluster: Cluster Analysis Basics and Extensions. R package version 2.1.8.1. doi:10.32614/CRAN.package.cluster
Bhat Kapu, S., & Kiruthika, C. (2025). Block Probabilistic Distance Clustering: A Unified Framework and Evaluation. PREPRINT (Version 1) available at Research Square. doi:10.21203/rs.3.rs-6973596/v1