Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1311#discussion_r14862907
--- Diff: docs/mllib-clustering.md ---
@@ -69,7 +69,54 @@ 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 KMeansExample {
+ public static void main(String[] args) {
+ SparkConf conf = new SparkConf().setAppName("K-means Example");
+ JavaSparkContext sc = new JavaSparkContext(conf);
+
+ // Load and parse data
+ String path = "{SPARK_HOME}/data/kmeans_data.txt";
+ JavaRDD<String> data = sc.textFile(path);
+ JavaRDD<Vector> parsedData = data.map(
+ new Function<String, Vector>() {
+ public Vector call(String s) {
+ String[] sarray = s.split(" ");
+ double[] values = new double[sarray.length];
+ for (int i = 0; i < sarray.length; i++)
+ values[i] = Double.parseDouble(sarray[i]);
+ return Vectors.dense(values);
+ }
+ }
+ );
+
+ // Cluster the data into two classes using KMeans
+ int numClusters = 2;
+ int numIterations = 20;
+ KMeansModel clusters = KMeans.train(JavaRDD.toRDD(parsedData),
numClusters, numIterations);
--- End diff --
`JavaRDD.toRDD(parsedData)` -> `parsedData.rdd()` (simpler)
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