Spark ML – K-Means Clustering

R/ml_clustering_kmeans.R, R/ml_model_kmeans.R

ml_kmeans

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

Arguments	Description
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
init_mode	Initialization algorithm. This can be either “random” to choose random points as initial cluster centers, or “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.

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.

Examples

library(sparklyr)
 
sc <- spark_connect(master = "local") 
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE) 
ml_kmeans(iris_tbl, Species ~ .) 
#> K-means clustering with 2 clusters
#> 
#> Cluster centers:
#>   Sepal_Length Sepal_Width Petal_Length Petal_Width
#> 1     6.301031    2.886598     4.958763    1.695876
#> 2     5.005660    3.369811     1.560377    0.290566
#> 
#> Within Set Sum of Squared Errors =  not computed.