Perform pam or vegclust clustering on a data sample — perform_sample

Function to perform pam in a hard or fuzzy way on a data sample

Usage

perform_sample_clustering(
  dist,
  data,
  clusters,
  type,
  metric,
  names,
  m = 2,
  build = FALSE,
  verbose = 1,
  verbose_toLogFile = FALSE,
  ...
)

Arguments

dist: Dissimilarity matrix
data: Data sample
clusters: Number of clusters
type: Hard or fuzzy clustering
metric: A character specifying a predefined dissimilarity metric (like "euclidean" or "manhattan") or a self-defined dissimilarity function. Defaults to "euclidean". Will be passed as argument method to dist, so check ?proxy::dist for full details.
names: Vector of names for observations
m: Fuzziness exponent (only for type = fuzzy)
build: Additional build algorithm to choose initial medoids (only relevant for type = "fuzzy". Default FALSE.)
verbose: Can be set to integers between 0 and 2 to control the level of detail of the printed diagnostic messages. Higher numbers lead to more detailed messages. Defaults to 1.
verbose_toLogFile: If TRUE, the diagnostic messages are printed to a log file clustering_progress.log. Defaults to FALSE.
...: Additional arguments passed to the main clustering algorithm (pam or vegclust)

Value

List with information on cluster results (medoid and cluster assignment)