ml_kmeans
From sparklyr v0.3.10
by Javier Luraschi
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 = ml_options(), ...)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-meansmodel 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_optionsfor 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
Community examples
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