Heuristic partitioning to minimise the expected loss function
with respect to a given expected adjacency matrix. This function is built upon R-package salso's implementation of the
salso function. See salso salsoAntMAN for more details.
AM_salso(
eam,
loss,
maxNClusters = 0,
nRuns = 16,
maxZealousAttempts = 10,
probSequentialAllocation = 0.5,
nCores = 0
)a co-clustering/ clustering matrix. See salso for more information on which matrix to use.
the recommended loss functions to be used are the "binder" or "VI". However, other loss functions that are supported can be found in the R-package salso's salso function.
Maximum number of clusters to be considered. The actual number of clusters searched may be lower. Default is 0.
Number of runs to try.
Maximum number of tries for zealous updates. See salso for more information.
The probability of using sequential allocation instead of random sampling via sample(1:K,ncol(x),TRUE), where K is maxNClusters. Default is 0.5. See salso for more information. argument.
Number of CPU cores to engage. Default is 0.
A numeric vector describing the estimated partition. The integer values represent the cluster labels of each item respectively.