Nothing
clusterDataset <- function(X, K, D = 0, methods = list(kmeansKA)) {
if (!is.list(methods)) {
methods = list(methods)
}
U = list()
for (ms in 1:length(methods)) {
if (!is.list(methods[[ms]])) {
methods[[ms]] = list(methods[[ms]])
}
# Get the method's function
meth = methods[[ms]][[1]]
# Get the name of the output variable if provided
if (is.null(methods[[ms]]$outputVariable)) {
outputVariable = NULL
} else {
outputVariable = methods[[ms]]$outputVariable
methods[[ms]]$outputVariable <- NULL
}
# Get the parameters and run the method
if (length(meth) > 1) {
params = methods[[ms]][2:length(methods[[ms]])]
res = meth(X, K, params)
} else {
params = NULL
res = meth(X, K)
}
# Get the output variable from the result if relevant
if (!is.null(outputVariable)) {
res = res[[outputVariable]]
}
# Convert the result to a partition matrix if not already so
if (is.vector(res) && max(res) > 1) {
res = clustVec2partMat(res)
}
# Store the result
U[[ms]] = res
}
return(U)
}
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