k-medoids across a range, returning all internal cluster-quality measures

Usage

x_medoids(
  data,
  min_clusters = 2,
  max_clusters = 10,
  metric = "euclidean",
  sort_by = c("silhouette", "dissimilarity", "isolation", "diameter", "separation"),
  ...
)

Arguments

data: A data frame / tibble of numeric features.
min_clusters: Smallest k to try (≥ 2 – silhouette is undefined for k = 1).
max_clusters: Largest k to try.
metric: "euclidean", "manhattan", … as accepted by pam().
sort_by: Which measure to sort on. Choices: "silhouette" (default), "dissimilarity", "isolation", "diameter", "separation".
...: Extra arguments forwarded to cluster::pam().

Value

A tibble with one row per k, containing: • the scalar measures (columns above) • model – the pam object • data – original data + .cluster factor • clusinfo and silhouette_widths for full drill-down