gaussian_mixture_clusters: Generate clusters of data points from Gaussian-mixture-model...

In ConsensusClustering: Consensus Clustering

Generate clusters of data points from Gaussian-mixture-model distributions with randomly generated parameters

Description

Generate clusters of data points from Gaussian-mixture-model distributions with randomly generated parameters

Usage

gaussian_mixture_clusters(
  n = c(50, 50),
  dim = 2,
  sd.max = 0.1,
  sd.noise = 0.01,
  r.range = c(0.1, 1),
  mixture.range = c(1, 4),
  mixture.sep = 0.5
)

Arguments

n	vector of number of data points in each cluster The length of n should be equal to the number of clusters.
dim	number of dimensions
sd.max	maximum standard deviation of clusters
sd.noise	standard deviation of the added noise
r.range	the range (min, max) of distance of cluster centers from the origin
mixture.range	range (min, max) of the number of Gaussian-mixtures.
mixture.sep	scaler indicating the separability between the mixtures.

Value

a list of data points (X) and cluster labels (class)

Examples

data = gaussian_mixture_clusters()
X = data$X
y = data$class

ConsensusClustering documentation built on Sept. 11, 2024, 6:38 p.m.