sparklyr (version 1.8.4)

ml_kmeans: Spark ML -- K-Means Clustering

Description

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+.

Usage

ml_kmeans(
  x,
  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)
ml_compute_silhouette_measure(
  model,
  dataset,
  distance_measure = c("squaredEuclidean", "cosine")
)

Value

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.

ml_compute_silhouette_measure() returns the Silhouette measure of the clustering on the given data.

Arguments

x

A spark_connection, ml_pipeline, or a tbl_spark.

formula

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.

k

The number of clusters to create

max_iter

The maximum number of iterations to use.

tol

Param for the convergence tolerance for iterative algorithms.

init_steps

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.

init_mode

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||.

seed

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

features_col

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_col

Prediction column name.

uid

A character string used to uniquely identify the ML estimator.

...

Optional arguments, see Details.

model

A fitted K-means model returned by ml_kmeans()

dataset

Dataset on which to calculate K-means cost

distance_measure

Distance measure to apply when computing the Silhouette measure.

See Also

See https://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()

Examples

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