k nearest neighbor algorithm for multi-variate data
knn(X, K = 2, init, Ninit = 50, verbose = FALSE, tol,
Niter.max = 500)data matrix, i.e. observations X dimensions
number of clusters to use
list of p and mu used for initialization
number of samples used per cluster if no init argument is given
allows print out of progress information; in verbose mode the cluster memberships are added to the output
smaller changes than tol in the objective function indicate convergence, if missing chosen automatically to be 1/5 of the smallest sample variance per dimension
maximum number of admissible iterations