Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/16566#discussion_r97014380
--- Diff: R/pkg/R/mllib_clustering.R ---
@@ -38,6 +45,149 @@ 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 seed the random seed.
+#' @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.
+#' Note that it is an advanced. The default
value should be enough
--- End diff --
as far as I recall the term used in spark.ml doc is "expert parameter" -
you might want to check how it is explained there.
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