part_kmeans: Partitioner: K-means, ICC, scaled means
Description
Partitioners are functions that tell the partition algorithm 1)
what to try to reduce 2) how to measure how much information is lost from
the reduction and 3) how to reduce the data. In partition, functions that
handle 1) are called directors, functions that handle 2) are called
metrics, and functions that handle 3) are called reducers. partition has a
number of pre-specified partitioners for agglomerative data reduction.
Custom partitioners can be created with as_partitioner().
Pass partitioner objects to the partitioner argument of partition().
part_kmeans() uses the following direct-measure-reduce approach:
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.
search
The search method. Binary search is generally more efficient
but linear search can be faster in very low dimensions.
init_k
The initial k to test. If NULL, then the initial k is the
threshold times the number of variables.
n_hits
In linear search method, the number of iterations that should
be under the threshold before reducing; useful for preventing false
positives.
See Also
Other partitioners:
as_partitioner(),
part_icc(),
part_minr2(),
part_pc1(),
part_stdmi(),
replace_partitioner()