sparklyr: ml_kmeans – R documentation

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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)

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

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.

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)

sparklyr

R Interface to Apache Spark

v1.6.2

Apache License 2.0 | file LICENSE

Authors

Javier Luraschi [aut], Kevin Kuo [aut] (<https://orcid.org/0000-0001-7803-7901>), Kevin Ushey [aut], JJ Allaire [aut], Samuel Macedo [ctb], Hossein Falaki [aut], Lu Wang [aut], Andy Zhang [aut], Yitao Li [aut, cre] (<https://orcid.org/0000-0002-1261-905X>), Jozef Hajnala [ctb], Maciej Szymkiewicz [ctb] (<https://orcid.org/0000-0003-1469-9396>), Wil Davis [ctb], RStudio [cph], The Apache Software Foundation [aut, cph]

Initial release