cuda_ml_kmeans: Run the k-means clustering algorithm.

In cuda.ml: R Interface for the RAPIDS cuML Suite of Libraries

Run the k-means clustering algorithm.

Description

Run the k-means clustering algorithm.

Usage

cuda_ml_kmeans(
  x,
  k,
  max_iters = 300,
  tol = 0,
  init_method = c("kmeans++", "random"),
  seed = 0L
)

Arguments

x	The input matrix or data frame. Each data point should be a row and should consist of numeric values only.
k	The number of clusters.
max_iters	Maximum number of iterations. Default: 300.
tol	Relative tolerance with regards to inertia to declare convergence. Default: 0 (i.e., do not use inertia-based stopping criterion).
init_method	Method for initializing the centroids. Valid methods include "kmeans++", "random", or a matrix of k rows, each row specifying the initial value of a centroid. Default: "kmeans++".
seed	Seed to the random number generator. Default: 0.

Value

A list containing the cluster assignments and the centroid of each cluster. Each centroid will be a column within the centroids matrix.

Examples

library(cuda.ml)

if (interactive() && cuda_ml_backend_info()$runtime_installed) {
  oils <- modeldata::oils
  oil_predictors <- oils |>
    subset(select = -class) |>
    scale()

  kclust <- cuda_ml_kmeans(
    oil_predictors,
    k = 7, max_iters = 100
  )

  print(kclust)
}

cuda.ml documentation built on Aug. 21, 2026, 9:14 a.m.