diceR (version 3.0.0)

majority_voting: Majority voting

Description

Combine clustering results using majority voting.

Usage

majority_voting(E, is.relabelled = TRUE)

Value

a vector of cluster assignments based on majority voting

Arguments

E

a matrix of clusterings with number of rows equal to the number of cases to be clustered, number of columns equal to the clustering obtained by different resampling of the data, and the third dimension are the different algorithms. Matrix may already be two-dimensional.

is.relabelled

logical; if FALSE the data will be relabelled using the first clustering as the reference.

Author

Aline Talhouk

Details

Combine clustering results generated using different algorithms and different data perturbations by majority voting. The class of a sample is the cluster label which was selected most often across algorithms and subsamples.

See Also

Other consensus functions: CSPA(), LCA(), LCE(), k_modes()

Examples

data(hgsc)
dat <- hgsc[1:100, 1:50]
cc <- consensus_cluster(dat, nk = 4, reps = 6, algorithms = "pam", progress =
FALSE)
table(majority_voting(cc[, , 1, 1, drop = FALSE], is.relabelled = FALSE))