ml_kmeans

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Spark ML -- K-Means Clustering

Perform k-means clustering on a Spark DataFrame.

Usage
ml_kmeans(x, centers, iter.max = 100, features = dplyr::tbl_vars(x), compute.cost = TRUE, tolerance = 1e-04, ml.options = NULL, ...)
Arguments
x
An object coercable to a Spark DataFrame (typically, a tbl_spark).
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 k-means model using Spark's computeCost.
tolerance
Param for the convergence tolerance for iterative algorithms.
ml.options
Optional arguments, used to affect the model generated. See ml_options for more details.
...
Optional arguments; currently unused.
Value

ml_model object of class kmeans with overloaded print, fitted and predict functions.

References

Bahmani et al., Scalable K-Means++, VLDB 2012

See Also

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

Aliases
  • ml_kmeans
Documentation reproduced from package sparklyr, version 0.3.6, License: file LICENSE

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