Run the K means clustering algorithm.

Source: R/kmeans.R

Run the K means clustering algorithm.

cuda_ml_kmeans(
  x,
  k,
  max_iters = 300,
  tol = 0,
  init_method = c("kmeans++", "random"),
  seed = 0L,
  cuML_log_level = c("off", "critical", "error", "warn", "info", "debug", "trace")
)

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.
cuML_log_level

Log level within cuML library functions. Must be one of "off", "critical", "error", "warn", "info", "debug", "trace". Default: off.

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)

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

print(kclust)

#> list()