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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