commute_cluster: Commute Time Clustering
In bbuchsbaum/neurocluster: Spatially contrained clustering for neuroimaging data

commute_cluster

R Documentation

Commute Time Clustering

Description

The commute_cluster function performs spatially constrained clustering on a NeuroVec instance using the commute time distance and K-means clustering.

Usage

commute_cluster(
  bvec,
  mask,
  K = 100,
  ncomp = ceiling(sqrt(K * 2)),
  alpha = 0.5,
  sigma1 = 0.73,
  sigma2 = 5,
  connectivity = 27,
  weight_mode = c("binary", "heat")
)

Arguments

`bvec`	A `NeuroVec` instance supplying the data to cluster.
`mask`	A `NeuroVol` mask defining the voxels to include in the clustering result. If the mask contains `numeric` data, nonzero values will define the included voxels. If the mask is a `LogicalNeuroVol`, then `TRUE` will define the set of included voxels.
`K`	The number of clusters to find. Default is 100.
`ncomp`	The number of components to use for the commute time embedding. Default is the ceiling of `sqrt(K2)`.
`alpha`	A numeric value controlling the balance between spatial and feature similarity. Default is 0.5.
`sigma1`	A numeric value controlling the spatial weighting function. Default is 0.73.
`sigma2`	A numeric value controlling the feature weighting function. Default is 5.
`connectivity`	An integer representing the number of nearest neighbors to consider when constructing the similarity graph. Default is 27.
`weight_mode`	A character string indicating the type of weight function for the similarity graph. Options are "binary" and "heat". Default is "heat".

Value

A list of class commute_time_cluster_result with the following elements:

clusvol: An instance of type ClusteredNeuroVol.
cluster: A vector of cluster indices equal to the number of voxels in the mask.
centers: A matrix of cluster centers with each column representing the feature vector for a cluster.
coord_centers: A matrix of spatial coordinates with each row corresponding to a cluster.

Examples

mask <- NeuroVol(array(1, c(20,20,20)), NeuroSpace(c(20,20,20)))
vec <- replicate(10, NeuroVol(array(runif(202020), c(20,20,20)),
NeuroSpace(c(20,20,20))), simplify=FALSE)
vec <- do.call(concat, vec)

commute_res <- commute_cluster(vec, mask, K=100)

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