ml_kmeans(x, centers, iter.max = 100, features = dplyr::tbl_vars(x), compute.cost = TRUE, tolerance = 1e-04, ml.options = ml_options(), ...)
tbl_spark
).k-means
model using Spark's computeCost.ml_options
for more details.data
argument can be used to
specify the data to be used when x
is a formula; this allows calls
of the form ml_linear_regression(y ~ x, data = tbl)
, and is
especially useful in conjunction with do
.kmeans
with overloaded print
, fitted
and predict
functions.
Other Spark ML routines: ml_als_factorization
,
ml_decision_tree
,
ml_generalized_linear_regression
,
ml_gradient_boosted_trees
,
ml_lda
, ml_linear_regression
,
ml_logistic_regression
,
ml_multilayer_perceptron
,
ml_naive_bayes
,
ml_one_vs_rest
, ml_pca
,
ml_random_forest
,
ml_survival_regression