[SPARK-19386][SPARKR][DOC] Bisecting k-means in SparkR documentation

## What changes were proposed in this pull request?
Update programming guide, example and vignette with Bisecting k-means.

Author: krishnakalyan3 <krishnakalyan3@gmail.com>

Closes #16767 from krishnakalyan3/bisecting-kmeans.

1 files changed, 14 insertions, 0 deletions

diff --git a/R/pkg/vignettes/sparkr-vignettes.Rmd b/R/pkg/vignettes/sparkr-vignettes.Rmd
index 36a78477dc..a7cac2f503 100644
--- a/R/pkg/vignettes/sparkr-vignettes.Rmd
+++ b/R/pkg/vignettes/sparkr-vignettes.Rmd
@@ -488,6 +488,8 @@ SparkR supports the following machine learning models and algorithms.
 
 #### Clustering
 
+* Bisecting $k$-means
+
 * Gaussian Mixture Model (GMM)
 
 * $k$-means Clustering
@@ -738,6 +740,18 @@ summary(rfModel)
 predictions <- predict(rfModel, df)
 ```
 
+#### Bisecting k-Means
+
+`spark.bisectingKmeans` is a kind of [hierarchical clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering) using a divisive (or "top-down") approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
+
+```{r}
+df <- createDataFrame(iris)
+model <- spark.bisectingKmeans(df, Sepal_Length ~ Sepal_Width, k = 4)
+summary(kmeansModel)
+fitted <- predict(model, df)
+head(select(fitted, "Sepal_Length", "prediction"))
+```
+
 #### Gaussian Mixture Model
 
 `spark.gaussianMixture` fits multivariate [Gaussian Mixture Model](https://en.wikipedia.org/wiki/Mixture_model#Multivariate_Gaussian_mixture_model) (GMM) against a `SparkDataFrame`. [Expectation-Maximization](https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm) (EM) is used to approximate the maximum likelihood estimator (MLE) of the model.