Github user chiwanpark commented on a diff in the pull request:
https://github.com/apache/flink/pull/1536#discussion_r50498429
 Diff: flinkexamples/flinkexamplesbatch/src/main/scala/org/apache/flink/examples/scala/clustering/KMeans.scala

@@ 26,53 +27,84 @@ import org.apache.flink.examples.java.clustering.util.KMeansData
import scala.collection.JavaConverters._
/**
 * This example implements a basic KMeans clustering algorithm.
 *
 * KMeans is an iterative clustering algorithm and works as follows:
 * KMeans is given a set of data points to be clustered and an initial set of ''K''
cluster
 * centers.
 * In each iteration, the algorithm computes the distance of each data point to each
cluster center.
 * Each point is assigned to the cluster center which is closest to it.
 * Subsequently, each cluster center is moved to the center (''mean'') of all points
that have
 * been assigned to it.
 * The moved cluster centers are fed into the next iteration.
 * The algorithm terminates after a fixed number of iterations (as in this implementation)
 * or if cluster centers do not (significantly) move in an iteration.
 * This is the Wikipedia entry for the [[http://en.wikipedia
 * .org/wiki/Kmeans_clustering KMeans Clustering algorithm]].
 *
 * This implementation works on twodimensional data points.
 * It computes an assignment of data points to cluster centers, i.e.,
 * each data point is annotated with the id of the final cluster (center) it belongs
to.
 *
 * Input files are plain text files and must be formatted as follows:
 *
 *  Data points are represented as two double values separated by a blank character.
 * Data points are separated by newline characters.
 * For example `"1.2 2.3\n5.3 7.2\n"` gives two data points (x=1.2, y=2.3) and (x=5.3,
 * y=7.2).
 *  Cluster centers are represented by an integer id and a point value.
 * For example `"1 6.2 3.2\n2 2.9 5.7\n"` gives two centers (id=1, x=6.2,
 * y=3.2) and (id=2, x=2.9, y=5.7).
 *
 * Usage:
 * {{{
 * KMeans <points path> <centers path> <result path> <num iterations>
 * }}}
 * If no parameters are provided, the program is run with default data from
 * [[org.apache.flink.examples.java.clustering.util.KMeansData]]
 * and 10 iterations.
 *
 * This example shows how to use:
 *
 *  Bulk iterations
 *  Broadcast variables in bulk iterations
 *  Custom Java objects (PoJos)
 */
+ * This example implements a basic KMeans clustering algorithm.
+ *
+ * KMeans is an iterative clustering algorithm and works as follows:
+ * KMeans is given a set of data points to be clustered and an initial set of ''K''
cluster
+ * centers.
+ * In each iteration, the algorithm computes the distance of each data point to each
cluster center.
+ * Each point is assigned to the cluster center which is closest to it.
+ * Subsequently, each cluster center is moved to the center (''mean'') of all points
that have
+ * been assigned to it.
+ * The moved cluster centers are fed into the next iteration.
+ * The algorithm terminates after a fixed number of iterations (as in this implementation)
+ * or if cluster centers do not (significantly) move in an iteration.
+ * This is the Wikipedia entry for the [[http://en.wikipedia
+ * .org/wiki/Kmeans_clustering KMeans Clustering algorithm]].
+ *
+ * This implementation works on twodimensional data points.
+ * It computes an assignment of data points to cluster centers, i.e.,
+ * each data point is annotated with the id of the final cluster (center) it belongs
to.
+ *
+ * Input files are plain text files and must be formatted as follows:
+ *
+ *  Data points are represented as two double values separated by a blank character.
+ * Data points are separated by newline characters.
+ * For example `"1.2 2.3\n5.3 7.2\n"` gives two data points (x=1.2, y=2.3) and (x=5.3,
+ * y=7.2).
+ *  Cluster centers are represented by an integer id and a point value.
+ * For example `"1 6.2 3.2\n2 2.9 5.7\n"` gives two centers (id=1, x=6.2,
+ * y=3.2) and (id=2, x=2.9, y=5.7).
+ *
+ * Usage:
+ * {{{
+ * KMeans <points path> <centers path> <result path> <num iterations>
+ * }}}
+ * If no parameters are provided, the program is run with default data from
+ * [[org.apache.flink.examples.java.clustering.util.KMeansData]]
+ * and 10 iterations.
+ *
+ * This example shows how to use:
+ *
+ *  Bulk iterations
+ *  Broadcast variables in bulk iterations
+ *  Custom Java objects (PoJos)
 End diff 
We're using "Scala objects". Could you change this line?

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