consensus.diss {yaConsensus}

Computes the consensus dissimilarity matrix.

Description

Computes the consensus dissimilarity according to the algorithm of Monti et al. (2003).

Usage

consensus.diss(cclusters, similarity = FALSE)

Arguments

cclusters

a matrix of integers where the column are the samples, and the rows are different clusterings of the samples.

similarity

a logical value signaling if the similarity matrix is required.

Details

In any row of the ccluster matrix, the value 0 means that the corresponding sample is not assigned to any cluster. In this case, the dissimilarity is computed consistently.

Value

An object of the 'dist' class.

Author(s)

Stefano M. Pagnotta

References

Monti et al. (2003) - Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data - Machine Learning 52(1-2):91-118 <DOI: 10.1023/A:1023949509487>

See Also

dist

Examples

clusters <- rep(1:3, c(3, 9, 18))
clusterings <- matrix(NA, ncol = 30, nrow = 50)
for(k in 1:50) clusterings[k,] <- sample(clusters)
ddist <- consensus.diss(clusterings)
class(ddist)
attr(ddist, "method")