Github user yinxusen commented on a diff in the pull request:
https://github.com/apache/spark/pull/11124#discussion_r52635130
--- Diff: R/pkg/R/mllib.R ---
@@ -126,3 +126,27 @@ setMethod("summary", signature(object =
"PipelineModel"),
return(list(coefficients = coefficients))
}
})
+
+#' Fit a k-means model
+#'
+#' Fit a k-means model, similarly to R's kmeans().
+#'
+#' @param x DataFrame for training
+#' @param centers Number of centers
+#' @param iter.max Maximum iteration number
+#' @param algorithm Algorithm choosen to fit the model
+#' @return A fitted k-means model
+#' @rdname kmeans
+#' @export
+#' @examples
+#'\dontrun{
+#' model <- kmeans(x, centers = 2, algorithm="random")
+#'}
+setMethod("kmeans", signature(x = "DataFrame"),
--- End diff --
I don't think we can make the signature the same as stats:kmeans for now,
since `KMeans` in MLlib doesn't provide the same interface with R's. E.g.
`KMeans` in MLlib doesn't support warm-start so the `centers` has only to be a
"numeric", and it doesn't have arguments like `nstart` and `trace`. Refer to
[the implementation of `glm` in
SparkR](https://github.com/apache/spark/blob/master/R/pkg/R/mllib.R#L53), which
also cannot match the signature with R's 100%.
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