WOAkMedoids (version 0.2.2)

woa_kmedoids: 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
)

Value

The `woa_clustering` object containing the clustering result and medoids

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)

Author

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)