HDBSCAN Clustering — petal_hdbscan • petalcluster

Hierarchical density-based spatial clustering of applications with noise.

Usage

petal_hdbscan(
  x,
  alpha = 1,
  min_samples = 15L,
  min_cluster_size = 15L,
  metric = c("euclidean", "cosine"),
  boruvka = TRUE,
  partial_labels = NULL
)

Arguments

x: A numeric matrix or data frame. Data frames are coerced to a matrix using their numeric columns (non-numeric columns are dropped).
alpha: Smoothing parameter for mutual reachability distance. Default 1.0.
min_samples: Minimum neighbourhood size. Default 15L.
min_cluster_size: Minimum cluster size. Default 15L.
metric: Distance metric, one of "euclidean" or "cosine".
boruvka: Whether to use Boruvka's algorithm for MST construction. Default TRUE.
partial_labels: Optional named list for semi-supervised clustering. Names are cluster IDs (as strings), values are integer vectors of 1-indexed point indices. NULL (default) for fully unsupervised clustering.

Value

An object of class "petal_hdbscan": a list with components cluster (integer vector of cluster IDs, NA for noise), n_clusters, n_noise, data (the input matrix), params, metric, and outlier_scores (GLOSH scores).