MiniBatchKmeans: A randomized dataset sub-sample algorithm that approximates...

In clusternor: A Parallel Clustering Non-Uniform Memory Access ('NUMA') Optimized Package

Description

A randomized dataset sub-sample algorithm that approximates the k-means algorithm. See: https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf

Usage

MiniBatchKmeans(
  data,
  centers,
  nrow = -1,
  ncol = -1,
  batch.size = 100,
  iter.max = .Machine$integer.max,
  nthread = -1,
  init = c("kmeanspp", "random", "forgy", "none"),
  tolerance = 0.01,
  dist.type = c("sqeucl", "eucl", "cos", "taxi"),
  max.no.improvement = 3
)

Arguments

data	Data file name on disk (NUMA optimized) or In memory data matrix
centers	Either (i) The number of centers (i.e., k), or (ii) an In-memory data matrix, or (iii) A 2-Element list with element 1 being a filename for precomputed centers, and element 2 the number of centroids.
nrow	The number of samples in the dataset
ncol	The number of features in the dataset
batch.size	Size of the mini batches
iter.max	The maximum number of iteration of k-means to perform
nthread	The number of parallel threads to run
init	The type of initialization to use c("kmeanspp", "random", "forgy", "none")
tolerance	The convergence tolerance
dist.type	What dissimilarity metric to use
max.no.improvement	Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed inertia

Value

A list containing the attributes of the output. cluster: A vector of integers (from 1:k) indicating the cluster to which each point is allocated. centers: A matrix of cluster centres. size: The number of points in each cluster. iter: The number of (outer) iterations.

Author(s)

Disa Mhembere <[e-mail]>

Examples

iris.mat <- as.matrix(iris[,1:4])
k <- length(unique(iris[, dim(iris)[2]])) # Number of unique classes
kms <- MiniBatchKmeans(iris.mat, k, batch.size=5)

clusternor documentation built on March 26, 2020, 7:31 p.m.