.k_means_fit_ClusterR: Simple Wrapper around ClusterR kmeans

Description

This wrapper runs ClusterR::KMeans_rcpp() and adds column names to the centroids field. And reorders the clusters.

Usage

.k_means_fit_ClusterR(
  data,
  clusters,
  num_init = 1,
  max_iters = 100,
  initializer = "kmeans++",
  fuzzy = FALSE,
  verbose = FALSE,
  CENTROIDS = NULL,
  tol = 1e-04,
  tol_optimal_init = 0.3,
  seed = 1
)

Value

a list with the following attributes: clusters, fuzzy_clusters (if fuzzy = TRUE), centroids, total_SSE, best_initialization, WCSS_per_cluster, obs_per_cluster, between.SS_DIV_total.SS

Arguments

data: matrix or data frame
clusters: the number of clusters
num_init: number of times the algorithm will be run with different centroid seeds
max_iters: the maximum number of clustering iterations
initializer: the method of initialization. One of, optimal_init, quantile_init, kmeans++ and random. See details for more information
fuzzy: either TRUE or FALSE. If TRUE, then prediction probabilities will be calculated using the distance between observations and centroids
verbose: either TRUE or FALSE, indicating whether progress is printed during clustering.
CENTROIDS: a matrix of initial cluster centroids. The rows of the CENTROIDS matrix should be equal to the number of clusters and the columns should be equal to the columns of the data.
tol: a float number. If, in case of an iteration (iteration > 1 and iteration < max_iters) 'tol' is greater than the squared norm of the centroids, then kmeans has converged
tol_optimal_init: tolerance value for the 'optimal_init' initializer. The higher this value is, the far appart from each other the centroids are.
seed: integer value for random number generator (RNG)