Run the k-means clustering algorithm.

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 dataframe. 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) {
  kclust <- cuda_ml_kmeans(
    iris[names(iris) != "Species"],
    k = 3, max_iters = 100
  )

  print(kclust)
}