ml_kmeans {sparklyr} | R Documentation |
Perform k-means clustering on a Spark DataFrame.
ml_kmeans(x, centers, iter.max = 100, features = dplyr::tbl_vars(x), compute.cost = TRUE, tolerance = 1e-04, ml.options = ml_options(), ...)
x |
An object coercable to a Spark DataFrame (typically, a
|
centers |
The number of cluster centers to compute. |
iter.max |
The maximum number of iterations to use. |
features |
The name of features (terms) to use for the model fit. |
compute.cost |
Whether to compute cost for |
tolerance |
Param for the convergence tolerance for iterative algorithms. |
ml.options |
Optional arguments, used to affect the model generated. See
|
... |
Optional arguments. The |
ml_model object of class kmeans
with overloaded print
, fitted
and predict
functions.
Bahmani et al., Scalable K-Means++, VLDB 2012
For information on how Spark k-means clustering is implemented, please see http://spark.apache.org/docs/latest/mllib-clustering.html#k-means.
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