diceR (version 1.2.0)

CSPA: Cluster-based Similarity Partitioning Algorithm (CSPA)

Description

Performs hierarchical clustering on a stack of consensus matrices to obtain consensus class labels.

Usage

CSPA(E, k)

Value

cluster assignments for the consensus class

Arguments

E

is an array of clustering results.

k

number of clusters

Author

Derek Chiu

See Also

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

Examples

data(hgsc)
dat <- hgsc[1:100, 1:50]
x <- consensus_cluster(dat, nk = 4, reps = 4, algorithms = c("hc", "diana"),
progress = FALSE)
CSPA(x, k = 4)