ml_kmeans: Spark ML - K-Means Clustering

In rstudio/sparklyr: R Interface to Apache Spark

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")
)

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. #' @return The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the predictor appended to it. If a tbl_spark, it will return a tbl_spark with the predictions added to it.
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.

Value

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.

Examples

## Not run: 
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
ml_kmeans(iris_tbl, Species ~ .)

## End(Not run)

rstudio/sparklyr documentation built on March 29, 2024, 3:30 p.m.