ClusterR (version 1.3.3)

KMeans_arma: k-means using the Armadillo library

Description

k-means using the Armadillo library

Usage

KMeans_arma(
  data,
  clusters,
  n_iter = 10,
  seed_mode = "random_subset",
  verbose = FALSE,
  CENTROIDS = NULL,
  seed = 1
)

Value

the centroids as a matrix. In case of Error it returns the error message, whereas in case of an empty centroids-matrix it returns a warning-message.

Arguments

data

matrix or data frame

clusters

the number of clusters

n_iter

the number of clustering iterations (about 10 is typically sufficient)

seed_mode

how the initial centroids are seeded. One of, keep_existing, static_subset, random_subset, static_spread, random_spread.

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. CENTROIDS should be used in combination with seed_mode 'keep_existing'.

seed

integer value for random number generator (RNG)

Details

This function is an R implementation of the 'kmeans' class of the Armadillo library. It is faster than the KMeans_rcpp function but it lacks some features. For more info see the details section of the KMeans_rcpp function. The number of columns should be larger than the number of clusters or CENTROIDS. If the clustering fails, the means matrix is reset and a bool set to false is returned. The clustering will run faster on multi-core machines when OpenMP is enabled in your compiler (eg. -fopenmp in GCC)

References

http://arma.sourceforge.net/docs.html

Examples

data(dietary_survey_IBS)

dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]

dat = center_scale(dat)

km = KMeans_arma(dat, clusters = 2, n_iter = 10, "random_subset")