Wrapper function to generate EVoC parameters

Usage

params_evoc(
  noise_level = 0.5,
  n_epochs = 50L,
  embedding_dim = NULL,
  neighbour_scale = 1,
  symmetrise = TRUE,
  min_samples = 5L,
  base_min_cluster_size = 5L,
  approx_n_clusters = NULL,
  min_similarity_threshold = 0.2,
  max_layers = 10L
)

Arguments

noise_level: Numeric. Noise level for the embedding gradient. 0.0 = aggressive, 1.0 = conservative. Defaults to 0.5.
n_epochs: Integer. Number of embedding optimisation epochs. Defaults to 50L.
embedding_dim: Integer or NULL. Embedding dimensionality. If NULL, defaults to min(max(n_neighbours / 4, 4), 16). Defaults to NULL.
neighbour_scale: Numeric. Multiplier on effective neighbours for fuzzy graph construction. Defaults to 1.0.
symmetrise: Logical. Whether to symmetrise the fuzzy graph. Defaults to TRUE.
min_samples: Integer. Minimum samples for core distance in MST density estimation. Defaults to 5L.
base_min_cluster_size: Integer. Base minimum cluster size for the finest layer. Defaults to 5L.
approx_n_clusters: Integer or NULL. If set, binary-searches for approximately this many clusters (single layer output). Defaults to NULL.
min_similarity_threshold: Numeric. Jaccard similarity threshold for filtering redundant layers. Defaults to 0.2.
max_layers: Integer. Maximum number of cluster layers to return. Defaults to 10L.

Value

A list with the EVoC parameters.