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
)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
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++')