Generate hierarchical cluster data

Generates synthetic data with a two-level cluster hierarchy: n_supergroups top-level groups each containing n_subclusts tight subclusters. Supergroup centres are spread far apart; subcluster centres sit tightly around their supergroup centre.

Note that the actual number of samples returned may be slightly less than n_samples if it is not evenly divisible by n_supergroups * n_subclusts.

Usage

rs_data_hierarchical(
  n_samples,
  dim,
  n_supergroups,
  n_subclusts,
  supergroup_spread,
  subcluster_spread,
  point_std,
  seed
)

Arguments

n_samples

Integer. Total number of points, distributed evenly across all subclusters.

dim

Integer. Dimensionality of the ambient space.

n_supergroups

Integer. Number of top-level groups. Defaults to 3.

n_subclusts

Integer. Number of subclusters per supergroup. Defaults to 3.

supergroup_spread

Numeric. Spread of supergroup centres. Defaults to 15.0.

subcluster_spread

Numeric. Spread of subcluster centres around their supergroup centre. Defaults to 2.0.

point_std

Numeric. Within-subcluster Gaussian noise. Defaults to 0.4.

seed

Integer. Seed for reproducibility.

Value

A named list with three elements: data, a numeric matrix of shape samples x dim; supergroup, an integer vector of supergroup labels (0..n_supergroups) one per sample; and subgroup, an integer vector of subcluster labels (0..n_supergroups * n_subclusts) one per sample.