mini_batch: Mini_batch

In mbkmeans: Mini-batch K-means Clustering for Single-Cell RNA-seq

Description

Mini-batch-k-means for matrix-like objects

Usage

mini_batch(
  data,
  clusters,
  batch_size,
  max_iters,
  num_init = 1L,
  init_fraction = 1,
  initializer = "kmeans++",
  compute_labels = TRUE,
  calc_wcss = FALSE,
  early_stop_iter = 10L,
  verbose = FALSE,
  CENTROIDS = NULL,
  tol = 1e-04
)

Arguments

data	numeric or integer matrix-like object.
clusters	the number of clusters.
batch_size	the size of the mini batches.
max_iters	the maximum number of clustering iterations.
num_init	number of times the algorithm will be run with different centroid seeds.
init_fraction	percentage of data to use for the initialization centroids (applies if initializer is kmeans++ ). Should be a float number between 0.0 and 1.0.
initializer	the method of initialization. One of kmeans++ and random. See details for more information.
compute_labels	logical indicating whether to compute the final cluster labels.
calc_wcss	logical indicating whether the within-cluster sum of squares should be computed and returned (ignored if 'compute_labels = FALSE').
early_stop_iter	continue that many iterations after calculation of the best within-cluster-sum-of-squared-error.
verbose	logical indicating whether progress is printed on screen.
CENTROIDS	an optional 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	convergence tolerance.

Details

This function performs k-means clustering using mini batches. It was inspired by the implementation in https://github.com/mlampros/ClusterR.

The input matrix can be in any format supported by the 'DelayedArray' / 'beachmat' framework, including the matrix classes defined in the 'Matrix' package and the 'HDFMatrix' class.

There are two possible initializations.

kmeans++: kmeans++ initialization.

random: random selection of data rows as initial centroids.

Value

a list with the following attributes:

centroids: the final centroids;

WCSS_per_cluster (optional): the final per-cluster WCSS.

best_initialization: which initialization value led to the best WCSS solution;

iters_per_initialization: number of iterations per each initialization;

Clusters (optional): the final cluster labels.

References

Sculley, D., 2010, April. Web-scale k-means clustering. In Proceedings of the 19th international conference on World wide web (pp. 1177-1178). ACM.

Arthur, D. and Vassilvitskii, S., 2007, January. k-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms (pp. 1027-1035). Society for Industrial and Applied Mathematics.

Examples

data = matrix(1:30,nrow = 10)
mini_batch(data, 2, 10, 10)

mbkmeans documentation built on Nov. 15, 2020, 2:07 a.m.