[GitHub] spark pull request #16566: [SPARK-18821][SparkR]: Bisecting k-means wrapper ...

[felixcheung <gi...@git.apache.org>]

Github user felixcheung commented on a diff in the pull request:

    https://github.com/apache/spark/pull/16566#discussion_r96103542
  
    --- Diff: R/pkg/R/mllib_clustering.R ---
    @@ -38,6 +45,146 @@ setClass("KMeansModel", representation(jobj = "jobj"))
     #' @note LDAModel since 2.1.0
     setClass("LDAModel", representation(jobj = "jobj"))
     
    +#' Bisecting K-Means Clustering Model
    +#'
    +#' Fits a bisecting k-means clustering model against a Spark DataFrame.
    +#' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
    +#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
    +#'
    +#' @param data a SparkDataFrame for training.
    +#' @param formula a symbolic description of the model to be fitted. Currently only a few formula
    +#'                operators are supported, including '~', '.', ':', '+', and '-'.
    +#'                Note that the response variable of formula is empty in spark.bisectingKmeans.
    +#' @param k the desired number of leaf clusters. Must be > 1.
    +#'          The actual number could be smaller if there are no divisible leaf clusters.
    +#' @param maxIter maximum iteration number.
    +#' @param minDivisibleClusterSize The minimum number of points (if greater than or equal to 1.0)
    +#'                                or the minimum proportion of points (if less than 1.0) of a divisible cluster.
    +#' @param seed the random seed.
    +#' @param ... additional argument(s) passed to the method.
    +#' @return \code{spark.bisectingKmeans} returns a fitted bisecting k-means model.
    +#' @rdname spark.bisectingKmeans
    +#' @aliases spark.bisectingKmeans,SparkDataFrame,formula-method
    +#' @name spark.bisectingKmeans
    +#' @export
    +#' @examples
    +#' \dontrun{
    +#' sparkR.session()
    +#' data(iris)
    --- End diff --
    
    don't need `data(iris)`


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