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)