Nothing

```
cl_bag <-
function(x, B, k = NULL,
algorithm = "kmeans", parameters = NULL,
method = "DFBC1", control = NULL)
{
## Currently, method 'DFBC1' (Dudoit-Fridlyand BagClust1) is the
## only one available, and argument 'control' is ignored.
## Construct reference partition.
algorithm <- match.fun(algorithm)
reference <- eval(as.call(c(list(algorithm, x),
if(!is.null(k)) list(k),
parameters)))
## Construct bootstrap ensemble.
clusterings <-
cl_boot(x, B, k, algorithm, parameters, resample = TRUE)
## Construct Dudoit-Fridlyand BagClust1 consensus partitions,
## suitably generalized ...
## <NOTE>
## In principle, this could be turned into a "constructive" method
## for cl_consensus(), also allowing for weights (straightforward).
## E.g.,
## .cl_consensus_partition_DFBC1(clusterings, weights, control)
## where either 'control specifies a reference partition, or the
## first element of 'clusterings' is taken as such.
## </NOTE>
k <- max(sapply(c(clusterings, reference), n_of_classes))
M_ref <- cl_membership(reference, k)
M <- matrix(0, NROW(M_ref), k)
for(b in seq_len(B)) {
mem <- cl_membership(clusterings[[b]], k)
## Match classes to reference partition.
ind <- solve_LSAP(crossprod(M_ref, mem), maximum = TRUE)
M <- M + mem[, ind]
}
as.cl_partition(cl_membership(as.cl_membership(M / B), k))
}
```

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