base clustering method: c("kmeans", "pam", "custom")
range.k
vector of minimum and maximum values for k c(min, max)
sample.k.method
method for the choice of k at each repeat c("random", "silhouette")
rep
number of repeats
distance.method
method for distance calculation:
"euclidian", "maximum", "manhattan",
"canberra", "binary", "minkowski".
func
user-definrd function required if cluster.method = "custom".
The function needs two inputs of X and k.
Details
At each repeat, k is selected randomly or based on the best silhouette width from a discrete uniform distribution between range.k[1] and range.k[2].
Then clustering is applied and result is returned.