woa_kmedoids: Whale Optimization Algorithm for K-Medoids Clustering

In WOAkMedoids: Whale Optimization Algorithm for K-Medoids Clustering

Whale Optimization Algorithm for K-Medoids Clustering

Description

This function implements the Whale Optimization Algorithm (WOA) for K-Medoids clustering. Supported distance measures are Dynamic Time Warping (DTW) and Euclidean Distance (ED).

Usage

woa_kmedoids(
  data,
  ClusNum,
  distance_method = c("dtw", "ed"),
  learned_w = NULL,
  Max_iter = 200,
  n = 5,
  early_stopping = TRUE,
  patience = 5,
  verbose = FALSE
)

Arguments

data	Data matrix
ClusNum	Number of clusters
distance_method	Distance calculation method, either "dtw" or "ed"
learned_w	Window size for DTW (only used if distance_method is "dtw")
Max_iter	Maximum number of iterations (default is 200, it can be adjusted according to the size of the dataset)
n	Population size (number of whales, default is 5, it can be adjusted according to the size of the dataset)
early_stopping	Logical. If TRUE, stop early when the best solution converges (default is TRUE)
patience	Number of consecutive iterations without improvement before early stopping (default is 5)
verbose	Logical. If TRUE, print progress messages (default is FALSE)

Value

The 'woa_clustering' object containing the clustering result and medoids

Author(s)

Chenan Huang, Narumasa Tsutsumida

References

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.

Examples

# NOTE: This example only shows how to implement woa_kmedoids using sample data.
# Results do not suggest any meanings.
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)

WOAkMedoids documentation built on Feb. 18, 2026, 5:06 p.m.