Github user wangmiao1981 commented on a diff in the pull request: https://github.com/apache/spark/pull/16666#discussion_r97260863 --- Diff: R/pkg/R/mllib_clustering.R --- @@ -225,10 +225,12 @@ setMethod("spark.kmeans", signature(data = "SparkDataFrame", formula = "formula" #' @param object a fitted k-means model. #' @return \code{summary} returns summary information of the fitted model, which is a list. -#' The list includes the model's \code{k} (number of cluster centers), +#' The list includes the model's \code{k} (the configured number of cluster centers), #' \code{coefficients} (model cluster centers), -#' \code{size} (number of data points in each cluster), and \code{cluster} -#' (cluster centers of the transformed data). +#' \code{size} (number of data points in each cluster), \code{cluster} +#' (cluster centers of the transformed data), and \code{clusterSize} +#' (the actual number of cluster centers. When using initMode = "random", --- End diff -- OK. I will add it. For bisecting kmeans, I haven't found a case like this. This case only occurs when initMode is random and this behavior was due to one fix to kmeans implementation.
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