https://github.com/kskbhat/silhouette
Silhouette-Based Diagnostics for Standard, Soft, and Multi-Way Clustering
https://github.com/kskbhat/silhouette
classification cluster-analysis clustering-algorithm membership-probability proximity-measure silhouette
Last synced: 28 days ago
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Silhouette-Based Diagnostics for Standard, Soft, and Multi-Way Clustering
- Host: GitHub
- URL: https://github.com/kskbhat/silhouette
- Owner: kskbhat
- License: gpl-2.0
- Created: 2025-04-10T10:02:02.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-10-14T21:41:07.000Z (about 1 month ago)
- Last Synced: 2025-10-14T23:06:21.386Z (about 1 month ago)
- Topics: classification, cluster-analysis, clustering-algorithm, membership-probability, proximity-measure, silhouette
- Language: R
- Homepage: https://kskbhat.github.io/Silhouette/
- Size: 18 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE
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README
# Silhouette 
[](https://cran.r-project.org/package=Silhouette)[](https://app.codecov.io/gh/kskbhat/Silhouette)
[](https://github.com/kskbhat/Silhouette/releases)
[](https://github.com/kskbhat/Silhouette/actions/workflows/R-CMD-check.yaml) [](https://github.com/kskbhat/Silhouette/actions/workflows/pages/pages-build-deployment)
An [R](https://www.r-project.org/) package for silhouette-based diagnostics in standard, soft, and multi-way clustering.
Quantifies clustering quality by measuring both **cohesion within clusters** and **separation between clusters**. Implements advanced silhouette width computations for diverse clustering structures, including: simplified silhouette by [Van der Laan et al. (2003)](https://doi.org/10.1080/0094965031000136012), Probability of Alternative Cluster normalization methods by [Raymaekers & Rousseeuw (2022)](https://doi.org/10.1080/10618600.2022.2050249), fuzzy clustering and silhouette diagnostics using membership probabilities by [Campello & Hruschka (2006)](https://doi.org/10.1016/j.fss.2006.07.006), [Menardi (20011)](https://doi.org/10.1007/s11222-010-9169-0) and [Bhat & Kiruthika (2024)](https://doi.org/10.1080/23737484.2024.2408534), and multi-way clustering extensions such as block and tensor clustering by [Schepers et al. (2008)](https://doi.org/10.1007/s00357-008-9005-9) and [Bhat & Kiruthika (2025)](https://doi.org/10.21203/rs.3.rs-6973596/v1). Provides tools for computation and visualization based on [Rousseeuw (1987)](https://doi.org/10.1016/0377-0427(87)90125-7) to support robust and reproducible cluster diagnostics across standard, soft, and multi-way clustering settings.
**Note:** This package does not use the classical [Rousseeuw (1987)](https://doi.org/10.1016/0377-0427(87)90125-7) calculation directly.
------------------------------------------------------------------------
## ✅ Why This Package?
- **Unified & consistent**: Offers one coherent interface for crisp, soft, and multi-way clustering silhouette diagnostics.
- **Flexible**: Works with distance matrices, clustering outputs, or soft membership probabilities.
- **Advanced**: Implements newer normalization methods (PAC, db), handles soft clustering, and supports mode-wise silhouette aggregation.
- **Visualization**: Plot functions produce clear, customizable silhouette plots compatible with many clustering outputs and existing silhouette outputs from [`factoextra`](https://doi.org/10.32614/CRAN.package.factoextra), [`cluster`](https://doi.org/10.32614/CRAN.package.cluster) and [`drclust`](https://doi.org/10.32614/CRAN.package.drclust) R packages.
- **Comparability**: Summaries and plots make it easy to compare clustering algorithms or tune the number of clusters.
- **Interoperable**: All `Silhouette` class functions works with any clustering output that provides a proximity or membership probability matrix. Users can also supply a proximity matrix and a clustering function—*including S3 or S4 methods*—to let `Silhouette` class perform clustering and compute silhouettes internally in one step.
------------------------------------------------------------------------
## Installation
You can install the released version of `Silhouette` from GitHub using:
``` r
# Install devtools if needed
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
# Install from GitHub
devtools::install_github("kskbhat/Silhouette")
```
From CRAN, install via:
``` r
install.packages("Silhouette")
```
------------------------------------------------------------------------
## Usage
Usage of the main functions is demonstrated in the package examples and documentation.
For an intro, see the vignette `Silhouette`, which is available as
``` r
vignette("Silhouette")
```
You can access the vignette from the [Get started](https://kskbhat.github.io/Silhouette/articles/Silhouette.html) tab in the top navigation bar of the package's [website](https://kskbhat.github.io/Silhouette/).
------------------------------------------------------------------------
## Report a Bug or Request a Feature
If you encounter a bug or have an idea for a new feature in the **Silhouette** package, please let us know by opening an issue on GitHub:
- [Create an issue on GitHub](https://github.com/kskbhat/Silhouette/issues)
- For bugs: include a minimal reproducible example, describe the expected vs. actual behavior, and mention your R and package versions
- For feature requests: clearly describe the proposed feature, its purpose, and how it would benefit users
Your feedback and suggestions are valuable and help improve the package.