[SPARK-6517][MLLIB] Implement the Algorithm of Hierarchical Clustering

I implemented a hierarchical clustering algorithm again.  This PR doesn't include examples, documentation and spark.ml APIs. I am going to send another PRs later.
https://issues.apache.org/jira/browse/SPARK-6517

- This implementation based on a bi-sectiong K-means clustering.
    - It derives from the freeman-lab 's implementation
- The basic idea is not changed from the previous version. (#2906)
    - However, It is 1000x faster than the previous version through parallel processing.

Thank you for your great cooperation, RJ Nowling(rnowling), Jeremy Freeman(freeman-lab), Xiangrui Meng(mengxr) and Sean Owen(srowen).

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Author: Yu ISHIKAWA <yu-iskw@users.noreply.github.com>

Closes #5267 from yu-iskw/new-hierarchical-clustering.

1 files changed, 73 insertions, 0 deletions

diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaBisectingKMeansSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaBisectingKMeansSuite.java
new file mode 100644
index 0000000000..a714620ff7
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaBisectingKMeansSuite.java
@@ -0,0 +1,73 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.clustering;
+
+import java.io.Serializable;
+
+import com.google.common.collect.Lists;
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+
+public class JavaBisectingKMeansSuite implements Serializable {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", this.getClass().getSimpleName());
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+  }
+
+  @Test
+  public void twoDimensionalData() {
+    JavaRDD<Vector> points = sc.parallelize(Lists.newArrayList(
+      Vectors.dense(4, -1),
+      Vectors.dense(4, 1),
+      Vectors.sparse(2, new int[] {0}, new double[] {1.0})
+    ), 2);
+
+    BisectingKMeans bkm = new BisectingKMeans()
+      .setK(4)
+      .setMaxIterations(2)
+      .setSeed(1L);
+    BisectingKMeansModel model = bkm.run(points);
+    Assert.assertEquals(3, model.k());
+    Assert.assertArrayEquals(new double[] {3.0, 0.0}, model.root().center().toArray(), 1e-12);
+    for (ClusteringTreeNode child: model.root().children()) {
+      double[] center = child.center().toArray();
+      if (center[0] > 2) {
+        Assert.assertEquals(2, child.size());
+        Assert.assertArrayEquals(new double[] {4.0, 0.0}, center, 1e-12);
+      } else {
+        Assert.assertEquals(1, child.size());
+        Assert.assertArrayEquals(new double[] {1.0, 0.0}, center, 1e-12);
+      }
+    }
+  }
+}