cc_get_cluster {CrossClustering}

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

Description

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

Usage

cc_get_cluster(x, n_elem)

## Default S3 method:
cc_get_cluster(x, n_elem)

## S3 method for class '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

Author(s)

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")

### 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)


### cc_get_cluster directly from a crossclustering object
cc_get_cluster(toyres)