Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1311#discussion_r14696246
--- Diff: docs/mllib-clustering.md ---
@@ -69,7 +69,55 @@ println("Within Set Sum of Squared Errors = " + WSSSE)
All of MLlib's methods use Java-friendly types, so you can import and call
them there the same
way you do in Scala. The only caveat is that the methods take Scala RDD
objects, while the
Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD
to a Scala one by
-calling `.rdd()` on your `JavaRDD` object.
+calling `.rdd()` on your `JavaRDD` object. A standalone application example
+that is equivalent to the provided example in Scala is given bellow:
+
+{% highlight java %}
+import org.apache.spark.api.java.*;
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.clustering.KMeans;
+import org.apache.spark.mllib.clustering.KMeansModel;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.linalg.Vector;
+
+public class Classifier {
+ public static void main( String[] args ) {
--- End diff --
use two spaces for indentation and remove extra spaces inside `main(...)`
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