dot-k_means_fit_ClusterR: Simple Wrapper around ClusterR kmeans

.k_means_fit_ClusterRR Documentation

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
)

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)

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


tidyclust documentation built on July 3, 2024, 1:06 a.m.