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diceR

majority_voting

Majority voting

Description

Combine clustering results using majority voting.

Usage

majority_voting(E, is.relabelled = TRUE)

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.

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.

Value

a vector of cluster assignments based on majority voting

Author(s)

Aline Talhouk

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))
diceR

Diverse Cluster Ensemble in R

v1.0.3
MIT + file LICENSE
Authors
Derek Chiu [aut, cre], Aline Talhouk [aut], Johnson Liu [ctb, com]
Initial release

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