[SPARK-17389][ML][MLLIB] KMeans speedup with better choice of k-means|| init steps = 2

## What changes were proposed in this pull request?

Reduce default k-means|| init steps to 2 from 5. See JIRA for discussion.
See also #14948

## How was this patch tested?

Existing tests.

Author: Sean Owen <sowen@cloudera.com>

Closes #14956 from srowen/SPARK-17389.2.

Author: srowen

mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala

@@ -51,10 +51,10 @@ class KMeans private (
 
   /**
    * Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20, runs: 1,
-   * initializationMode: "k-means||", initializationSteps: 5, epsilon: 1e-4, seed: random}.
+   * initializationMode: "k-means||", initializationSteps: 2, epsilon: 1e-4, seed: random}.
    */
   @Since("0.8.0")
-  def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 5, 1e-4, Utils.random.nextLong())
+  def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 2, 1e-4, Utils.random.nextLong())
 
   /**
    * Number of clusters to create (k).
@@ -134,7 +134,7 @@ class KMeans private (
 
   /**
    * Set the number of steps for the k-means|| initialization mode. This is an advanced
-   * setting -- the default of 5 is almost always enough. Default: 5.
+   * setting -- the default of 2 is almost always enough. Default: 2.
    */
   @Since("0.8.0")
   def setInitializationSteps(initializationSteps: Int): this.type = {

mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala

@@ -49,7 +49,7 @@ class PowerIterationClusteringSuite extends SparkFunSuite with MLlibTestSparkCon
     val r1 = 1.0
     val n1 = 10
     val r2 = 4.0
-    val n2 = 40
+    val n2 = 10
     val n = n1 + n2
     val points = genCircle(r1, n1) ++ genCircle(r2, n2)
     val similarities = for (i <- 1 until n; j <- 0 until i) yield {
@@ -83,19 +83,15 @@ class PowerIterationClusteringSuite extends SparkFunSuite with MLlibTestSparkCon
     val r1 = 1.0
     val n1 = 10
     val r2 = 4.0
-    val n2 = 40
+    val n2 = 10
     val n = n1 + n2
     val points = genCircle(r1, n1) ++ genCircle(r2, n2)
     val similarities = for (i <- 1 until n; j <- 0 until i) yield {
       (i.toLong, j.toLong, sim(points(i), points(j)))
     }
 
     val edges = similarities.flatMap { case (i, j, s) =>
-      if (i != j) {
-        Seq(Edge(i, j, s), Edge(j, i, s))
-      } else {
-        None
-      }
+      Seq(Edge(i, j, s), Edge(j, i, s))
     }
     val graph = Graph.fromEdges(sc.parallelize(edges, 2), 0.0)