MiniBatchKmeans: Mini-batch-k-means using RcppArmadillo

In ClusterR: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering

Mini-batch-k-means using RcppArmadillo

Description

Mini-batch-k-means using RcppArmadillo

Usage

MiniBatchKmeans(
  data,
  clusters,
  batch_size = 10,
  num_init = 1,
  max_iters = 100,
  init_fraction = 1,
  initializer = "kmeans++",
  early_stop_iter = 10,
  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
batch_size	the size of the mini batches
num_init	number of times the algorithm will be run with different centroid seeds
max_iters	the maximum number of clustering iterations
init_fraction	percentage of data to use for the initialization centroids (applies if initializer is kmeans++ or optimal_init). Should be a float number between 0.0 and 1.0.
initializer	the method of initialization. One of, optimal_init, quantile_init, kmeans++ and random. See details for more information
early_stop_iter	continue that many iterations after calculation of the best within-cluster-sum-of-squared-error
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)

Details

This function performs k-means clustering using mini batches.

—————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

Value

a list with the following attributes: centroids, WCSS_per_cluster, best_initialization, iters_per_initialization

Author(s)

Lampros Mouselimis

References

http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf, https://github.com/siddharth-agrawal/Mini-Batch-K-Means

Examples

data(dietary_survey_IBS)

dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]

dat = center_scale(dat)

MbatchKm = MiniBatchKmeans(dat, clusters = 2, batch_size = 20, num_init = 5, early_stop_iter = 10)

ClusterR documentation built on June 22, 2024, 10:28 a.m.