Select the best fuzzy c-means partition across repeated initialisations

This function runs fuzzy c-means clustering (e1071::cmeans) repeatedly with different random seeds and selects the partition that maximises an AMD-like objective:

Usage

assign_clusters_best(
  data,
  opt_cluster,
  nreps = 10,
  m = 2,
  iter.max = 20,
  scale_data = FALSE,
  seeds = NULL,
  preselect_top_sd = NULL
)

Arguments

data

A numeric matrix or data frame of samples × features.

opt_cluster

Integer; number of clusters to fit.

nreps

Number of repeated initialisations.

m

Fuzziness parameter for fuzzy c-means (default 2).

iter.max

Maximum number of iterations for fuzzy c-means.

scale_data

Logical; if TRUE, standardise features before clustering.

seeds

Optional numeric vector of seeds for deterministic behaviour. Must have length nreps. If NULL, random seeds are drawn.

preselect_top_sd

Optional integer; if provided, only the top-SD features are retained before clustering (useful for very high-dimensional data).

Value

A list with components:

cluster

Integer vector of cluster labels aligned to the original data. Rows with missing values receive NA.

membership

Membership matrix from the best fuzzy c-means run.

centers

Cluster centroids from the best run.

Mpm

Best AMD-like objective value.

Details

$$ \mathrm{Mpm} = \mathrm{mean}(\max_i u_i) - 1/k $$

where \(u_i\) is the membership vector of sample \(i\). The best partition is returned, with cluster labels aligned to the original row order of the input data (rows with missing values receive NA).

Examples

if (FALSE) { # \dontrun{
set.seed(1)
X <- matrix(rnorm(1000), nrow = 100, ncol = 10)
out <- assign_clusters_best(X, opt_cluster = 3, nreps = 20)
table(out$cluster)
} # }