Cluster-based Similarity Partitioning Algorithm (CSPA)

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

Usage

CSPA(E, k)

Arguments

E

is an array of clustering results.

k

number of clusters

Value

cluster assignments for the consensus class

References

Strehl, A., & Ghosh, J. (2002). Cluster ensembles---a knowledge reuse framework for combining multiple partitions. Journal of machine learning research, 3(Dec), 583-617.

See also

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

Author

Derek Chiu

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)
#>   [1] 1 1 2 2 2 1 1 2 2 1 2 2 1 1 3 2 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 2
#>  [38] 2 3 2 1 2 2 2 2 2 2 2 1 1 2 2 1 1 1 2 2 1 2 1 1 1 2 1 1 4 1 2 2 1 2 1 1 2
#>  [75] 1 2 1 1 3 1 2 1 2 2 1 2 1 1 3 2 2 2 1 2 2 1 1 2 1 1