K-means clustering with support for k-means|| initialization proposed by Bahmani et al. Using `ml_kmeans()` with the formula interface requires Spark 2.0+.

  formula = NULL,
  k = 2,
  max_iter = 20,
  tol = 1e-04,
  init_steps = 2,
  init_mode = "k-means||",
  seed = NULL,
  features_col = "features",
  prediction_col = "prediction",
  uid = random_string("kmeans_"),

ml_compute_cost(model, dataset)



A spark_connection, ml_pipeline, or a tbl_spark.


Used when x is a tbl_spark. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.


The number of clusters to create


The maximum number of iterations to use.


Param for the convergence tolerance for iterative algorithms.


Number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 2 is almost always enough. Must be > 0. Default: 2.


Initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.


A random seed. Set this value if you need your results to be reproducible across repeated calls.


Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by ft_r_formula.


Prediction column name.


A character string used to uniquely identify the ML estimator.


Optional arguments, see Details.


A fitted K-means model returned by ml_kmeans()


Dataset on which to calculate K-means cost


The object returned depends on the class of x.

  • spark_connection: When x is a spark_connection, the function returns an instance of a ml_estimator object. The object contains a pointer to a Spark Estimator object and can be used to compose Pipeline objects.

  • ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the clustering estimator appended to the pipeline.

  • tbl_spark: When x is a tbl_spark, an estimator is constructed then immediately fit with the input tbl_spark, returning a clustering model.

  • tbl_spark, with formula or features specified: When formula is specified, the input tbl_spark is first transformed using a RFormula transformer before being fit by the estimator. The object returned in this case is a ml_model which is a wrapper of a ml_pipeline_model. This signature does not apply to ml_lda().

ml_compute_cost() returns the K-means cost (sum of squared distances of points to their nearest center) for the model on the given data.

See also

See http://spark.apache.org/docs/latest/ml-clustering.html for more information on the set of clustering algorithms.

Other ml clustering algorithms: ml_bisecting_kmeans(), ml_gaussian_mixture(), ml_lda()


if (FALSE) { sc <- spark_connect(master = "local") iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE) ml_kmeans(iris_tbl, Species ~ .) }