sbclust: Sample-based bagged clustering

In sdesabbata/sbclust: Sample-based bagged clustering

Sample-based bagged clustering

Description

A bagged clustering function derived from the bclust function of the e1071 package but using only a sample of the original dataset. See: https://cran.r-project.org/web/packages/e1071

Usage

sbclust(
  x,
  centers = 2,
  iter.base = 10,
  minsize = 0,
  dist.method = "euclidean",
  hclust.method = "average",
  base.method = "kmeans",
  base.centers = 20,
  verbose = TRUE,
  final.kmeans = FALSE,
  docmdscale = FALSE,
  resample = TRUE,
  sample_prop = 0.01,
  weights = NULL,
  maxcluster = base.centers,
  ...
)

Arguments

x	Matrix of inputs (or object of class "bclust" from the e1071 package for plot).
centers,	k Number of clusters.
iter.base	Number of runs of the base cluster algorithm.
minsize	Minimum number of points in a base cluster.
dist.method	Distance method used for the hierarchical clustering, see dist for available distances.
hclust.method	Linkage method used for the hierarchical clustering, see hclust for available methods.
base.method	Partitioning cluster method used as base algorithm.
base.centers	Number of centers used in each repetition of the base method.
verbose	Output status messages.
final.kmeans	If TRUE, a final kmeans step is performed using the output of the bagged clustering as initialization.
docmdscale	Logical, if TRUE a cmdscale result is included in the return value.
resample	Logical, if TRUE the base method is run on bootstrap samples of x, else directly on x.
sample_prop	Proportion of the whole dataset to sample. The default value 0.01 results in samples which are 1% of the size of the original dataset.
weights	Vector of length nrow(x), weights for the resampling. By default all observations have equal weight.
maxcluster	Maximum number of clusters memberships are to be computed for.
...	Optional arguments top be passed to the base method in sbclust, ignored in plot.

Value

return objects of class "bclust" from the e1071 package.

sdesabbata/sbclust documentation built on May 12, 2022, 7:07 a.m.