CrossClustering (version 4.1.2)

consensus_cluster: Get clusters which reach max consensus

Description

Computes the consensus between Ward's minimum variance and Complete-linkage (or Single-linkage) algorithms (i.e., the number of elements classified together by both algorithms).

Usage

consensus_cluster(k, cluster_ward, cluster_other)

Value

an object of class consensus_cluster with the following elements:

elements

list of the elements belonging to each cluster

;

a_star

contingency table of the clustering

;

max_consensus

maximum clustering consensus

.

Arguments

k

(int) a vector containing the number of clusters for Ward and for Complete-linkage (or Single-linkage) algorithms, respectively

cluster_ward

an object of class hclust for the Ward algorithm

cluster_other

an object of class hclust for the Complete-linkage (or Single-linkage) algorithm

Author

Paola Tellaroli, <paola dot tellaroli at unipd dot it>;; Marco Bazzi, <bazzi at stat dot unipd dot it>; Michele Donato, <mdonato at stanford dot edu>.

References

Tellaroli P, Bazzi M., Donato M., Brazzale A. R., Draghici S. (2016). Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters. PLoS ONE 11(3): e0152333. doi:10.1371/journal.pone.0152333

Examples

library(CrossClustering)

data(toy)

### toy is transposed as we want to cluster samples (columns of the
### original matrix)
toy_dist <- t(toy) |>
  dist(method = "euclidean")

### Hierarchical clustering
cluster_ward <- toy_dist |>
  hclust(method = "ward.D")
cluster_other <- toy_dist |>
  hclust(method = "complete")


### consensus_cluster
consensus_cluster(
  c(3, 4),
  cluster_ward,
  cluster_other
)