cuda_ml_kmeans {cuda.ml} | R Documentation |
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,
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)