This class performs expectation maximization for multivariate Gaussian Mixture Models (GMMs). A GMM represents a composite distribution of independent Gaussian distributions with associated "mixing" weights specifying each's contribution to the composite. Given a set of sample points, this class will maximize the log-likelihood for a mixture of k Gaussians, iterating until the log-likelihood changes by less than tol, or until it has reached the max number of iterations. While this process is generally guaranteed to converge, it is not guaranteed to find a global optimum.

  formula = NULL,
  k = 2,
  max_iter = 100,
  tol = 0.01,
  seed = NULL,
  features_col = "features",
  prediction_col = "prediction",
  probability_col = "probability",
  uid = random_string("gaussian_mixture_"),



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.


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.


Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.


A character string used to uniquely identify the ML estimator.


Optional arguments, see Details.


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().

See also

See for more information on the set of clustering algorithms.

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


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