Directors are functions that tell the partition algorithm what
to try to reduce. as_director() is a helper function to create new
directors to be used in partitioners. partitioners can be created with
as_partitioner().
direct_k_cluster() assigns each variable to a cluster using
K-means. As the partition looks for the best reduction,
direct_k_cluster() iterates through values of k to assign clusters.
This search is handled by the binary search method by default and thus
does not necessarily need to fit every value of k.
direct_k_cluster(.partition_step, algorithm = c("armadillo",
"Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"), search = c("binary",
"linear"), init_k = NULL, seed = 1L)a partition_step object
The K-Means algorithm to use. The default is a fast version
of the LLoyd algorithm written in armadillo. The rest are options in
kmeans(). In general, armadillo is fastest, but the other algorithms can
be faster in high dimensions.
The search method. Binary search is generally more efficient but linear search can be faster in very low dimensions.
The initial k to test. If NULL, then the initial k is the
threshold times the number of variables.
The seed to set for reproducibility
a partition_step object
Other directors: as_director,
direct_distance