Whale Optimization Algorithm for K-Medoids Clustering
Overview
Implements the Whale Optimization Algorithm(WOA) for k-medoids clustering, providing tools for effective and efficient cluster analysis in various data sets. The methodology is based on "The Whale Optimization Algorithm" by Mirjalili and Lewis (2016) doi:10.1016/j.advengsoft.2016.01.008.
Added
- Reimplemented performance-critical routines in C++ via Rcpp.
- Parallel fitness evaluation using RcppParallel.
- Early stopping mechanism to accelerate convergence.
Changed
- Fused cluster assignment and distance accumulation into a single loop for improved performance.
- Refactored internal WOA implementation for better maintainability.
Fixed
- Minor internal fixes.
From NEWS.md in the release
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-04-22
Show 2 earlier changesHide 2 earlier changes
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 6 (4 internal)
- Examples that run
- 100%2 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 33%
Downloads
Dependencies
Code & Tests
Datasets
People & History
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- RR 4.6.0 released · 2026-04-24
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- 0.1.02024-08-28
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2024-08-28
- Total releases
- 2 / 2 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 147 KB / 1 file
- Download size
- 159 KB
Cite
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Run in R for the authors' preferred citation:
citation("WOAkMedoids")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
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