Kmeans: Perform k-means clustering on a data matrix.

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

Description

K-means provides k disjoint sets for a dataset using a parallel and fast NUMA optimized version of Lloyd's algorithm. The details of which are found in this paper https://arxiv.org/pdf/1606.08905.pdf.

Usage

Kmeans(
  data,
  centers,
  nrow = -1,
  ncol = -1,
  iter.max = .Machine$integer.max,
  nthread = -1,
  init = c("kmeanspp", "random", "forgy", "none"),
  tolerance = 1e-06,
  dist.type = c("eucl", "sqeucl", "cos", "taxi")
)

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
nrow	The number of samples in the dataset
ncol	The number of features in the dataset
iter.max	The maximum number of iteration of k-means to perform
nthread	The number of parallel threads to run (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.
init	The type of initialization to use c("kmeanspp", "random", "forgy", "none")
tolerance	The convergence tolerance
dist.type	What dissimilarity metric to use

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 <- Kmeans(iris.mat, k)

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