Provides the vector of clusters' ID to which each element belong to.

Source: R/cc_get_cluster.R

Provides the vector of clusters' ID to which each element belong to.

cc_get_cluster(x, n_elem)

# S3 method for default
cc_get_cluster(x, n_elem)

# S3 method for crossclustering
cc_get_cluster(x, n_elem)

Arguments

x

list of clustered elements or a crossclustering object

n_elem

total number of elements clustered (ignored if x is of class crossclustering)

Value

An integer vector of clusters to which the elements belong (1

for the outliers, ID + 1 for the others).

Methods (by class)

  • cc_get_cluster(default): default method for cc_get_cluster.

  • cc_get_cluster(crossclustering): automatically extract inputs from a crossclustering object

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

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>.

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")

### Run CrossClustering
toyres <- cc_crossclustering(
  toy_dist,
  k_w_min = 2,
  k_w_max = 5,
  k2_max  = 6,
  out     = TRUE
)

### cc_get_cluster
cc_get_cluster(toyres[], 7)
#> [1] 2 2 3 3 4 4 1


### cc_get_cluster directly from a crossclustering object
cc_get_cluster(toyres)
#> [1] 2 2 3 3 4 4 1