consensus_matrix {ConsensusClustering}

Calculate consensus matrix for data perturbation consensus clustering

Description

Calculate consensus matrix for data perturbation consensus clustering

Usage

consensus_matrix(
  X,
  max.cluster = 5,
  resample.ratio = 0.7,
  max.itter = 100,
  clustering.method = "hclust",
  adj.conv = TRUE,
  verbos = TRUE
)

Arguments

X

adjacency matrix a Nsample x Nsample

max.cluster

maximum number of clusters

resample.ratio

the data ratio to use at each itteration.

max.itter

maximum number of itterations at each max.cluster

clustering.method

base clustering method: c("hclust", "spectral", "pam")

adj.conv

binary value to apply soft thresholding (default=TRUE)

verbos

binary value for verbosity (default=TRUE)

Details

performs data perturbation consensus clustering and obtain consensus matrix Monti et al. (2003) consensus clustering algorithm This function will be removed in the future release and is replaced by consensus_matrix_data_prtrb()

Value

list of consensus matrices for each k

Examples

X = gaussian_clusters()$X
Adj = adj_mat(X, method = "euclidian")
CM = consensus_matrix(Adj, max.cluster=3, max.itter=10, verbos = FALSE)