ClusterR (version 1.3.3)

KMeans_rcpp: k-means using RcppArmadillo

Description

k-means using RcppArmadillo

Usage

KMeans_rcpp(
  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)

Author

Lampros Mouselimis

Details

This function has the following features in comparison to the KMeans_arma function:

Besides optimal_init, quantile_init, random and kmeans++ initilizations one can specify the centroids using the CENTROIDS parameter.

The running time and convergence of the algorithm can be adjusted using the num_init, max_iters and tol parameters.

If num_init > 1 then KMeans_rcpp returns the attributes of the best initialization using as criterion the within-cluster-sum-of-squared-error.

---------------initializers----------------------

optimal_init : this initializer adds rows of the data incrementally, while checking that they do not already exist in the centroid-matrix [ experimental ]

quantile_init : initialization of centroids by using the cummulative distance between observations and by removing potential duplicates [ experimental ]

kmeans++ : kmeans++ initialization. Reference : http://theory.stanford.edu/~sergei/papers/kMeansPP-soda.pdf AND http://stackoverflow.com/questions/5466323/how-exactly-does-k-means-work

random : random selection of data rows as initial centroids

Examples

data(dietary_survey_IBS)

dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]

dat = center_scale(dat)

km = KMeans_rcpp(dat, clusters = 2, num_init = 5, max_iters = 100, initializer = 'kmeans++')