meta_clust.cluster_result: Meta Clustering for Cluster Results

In bbuchsbaum/neurocluster: Spatially contrained clustering for neuroimaging data

Meta Clustering for Cluster Results

Description

The meta_clust function performs meta clustering on a given clustering result by applying hierarchical clustering or other clustering algorithms.

Usage

## S3 method for class 'cluster_result'
meta_clust(
  x,
  cuts = min(as.integer(length(x$centers)/2), 2),
  algo = "hclust",
  hclust_method = "ward.D"
)

Arguments

x	A clustering result, typically an object of class "cluster_result".
cuts	The number of cluster cuts to consider. Default is the minimum of half the number of centers and 2.
algo	A character string indicating the clustering algorithm to use. Default is "hclust" (hierarchical clustering).
hclust_method	A character string specifying the agglomeration method to use for hierarchical clustering. Default is "ward.D".

Value

A list containing:

cvols	A list of ClusteredNeuroVol instances.
cuts	The number of cluster cuts.
cutmat	A matrix representing the cluster assignments for each cut.
hclus	The hierarchical clustering result.

See Also

hclust, cutree

bbuchsbaum/neurocluster documentation built on April 1, 2024, 8:43 p.m.