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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