Hmeans: Perform parallel hierarchical clustering on a data matrix.

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

Description

A recursive (not acutally implemented as recursion) partitioning of data into two disjoint sets at every level as described in https://en.wikipedia.org/wiki/Hierarchical_clustering

Usage

Hmeans(
  data,
  kmax,
  nrow = -1,
  ncol = -1,
  iter.max = 20,
  nthread = -1,
  init = c("forgy"),
  tolerance = 1e-06,
  dist.type = c("eucl", "cos", "sqeucl", "taxi"),
  min.clust.size = 1
)

Arguments

data	Data file name on disk (NUMA optmized) or In memory data matrix
kmax	The maximum number of centers
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
init	The type of initialization to use c("forgy") or initial centers
tolerance	The convergence tolerance for k-means at each hierarchical split
dist.type	What dissimilarity metric to use
min.clust.size	The minimum size of a cluster when it cannot be split

Value

A list of lists 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])
kmax <- length(unique(iris[, dim(iris)[2]])) # Number of unique classes
kms <- Hmeans(iris.mat, kmax)

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