gaussian_mixture_clusters {ConsensusClustering}

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