This function will select the optimal clustering result from several kmeans runs
with different random seeds, The clustering result with the smallest
within-cluster-sum of squared distances will be selected.
Usage
optimal_km(x, k, num_seeds = 10, iter_max = 10, ...)
Arguments
- x
Matrix. This function will cluster the rows of the input matrix.
- k
Integer. Number of cluters to detect for kmeans.
- num_seeds
Integer. Number of trials with random seeds
- iter_max
Integer. Number of iterations.
- ...
Further arguments handed to stats::kmeans
Value
Returns an object of class "kmeans" with is a list with several components
(see ?kmeans) which guves the local optimal kmeans clustering result within
#num_seeds trials.