https://github.com/woakmedoids/woakmedoids

An R package implementing Whale Optimization Algorithm for k-medoids clustering
https://github.com/woakmedoids/woakmedoids

clustering k-medoids r whale-optimization-algorithm

Last synced: about 2 months ago
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An R package implementing Whale Optimization Algorithm for k-medoids clustering

• Host: GitHub
• URL: https://github.com/woakmedoids/woakmedoids
• Owner: WOAkMedoids
• License: gpl-3.0
• Created: 2026-04-05T08:51:08.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2026-04-05T10:06:52.000Z (about 2 months ago)
• Last Synced: 2026-04-05T11:16:15.770Z (about 2 months ago)
• Topics: clustering, k-medoids, r, whale-optimization-algorithm
• Language: R
• Homepage:
• Size: 183 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: NEWS.md
  • License: LICENSE

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README

          
# WOAkMedoids

[![CRAN status](https://www.r-pkg.org/badges/version/WOAkMedoids)](https://CRAN.R-project.org/package=WOAkMedoids)

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)

An R package that implements the Whale Optimization Algorithm (WOA) for k-medoids clustering, providing tools for effective and efficient cluster analysis in various data sets. Supported distance measures include Dynamic Time Warping (DTW) and Euclidean Distance (ED).

## Installation

Install the released version from CRAN:

```r

install.packages("WOAkMedoids")

```

Or install the development version from GitHub:

```r

devtools::install_github("WOAkMedoids/woakmedoids")

```

## Usage

```r

library(WOAkMedoids)

data(Lightning7)

Lightning7_data <- Lightning7[, -1]  # Remove the first column of classification data

result <- woa_kmedoids(Lightning7_data, ClusNum = 7, distance_method = "dtw", learned_w = 5)

print(result)

```

## Reference

Chenan H. and Tsutsumida N. (2025) A scalable k-medoids clustering via whale optimization algorithm, *Array*, 28, 100599. [https://doi.org/10.1016/j.array.2025.100599](https://doi.org/10.1016/j.array.2025.100599)