Arguments
k
Determines the number of clusters.
d
Determines the number of dimensions.
center
A matrix of means for each dimension of each cluster.
size
A k
times d
matrix with the cube dimenstions.
p
A vector of probabilities that determines the likelihood of
generated a data point from a particular cluster.
noise
Noise probability between 0 and 1.
Noise is uniformly distributed within noise range (see below).
noise_range
A matrix with d rows and 2 columns. The first column
contains the minimum values and the second column contains the maximum
values for noise.