content: /*
   * 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.examples.mllib;
  
  import java.util.ArrayList;
  
  // $example on$
  import com.google.common.collect.Lists;
  // $example off$
  import org.apache.spark.SparkConf;
  import org.apache.spark.api.java.JavaSparkContext;
  // $example on$
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.mllib.clustering.BisectingKMeans;
  import org.apache.spark.mllib.clustering.BisectingKMeansModel;
  import org.apache.spark.mllib.linalg.Vector;
  import org.apache.spark.mllib.linalg.Vectors;
  // $example off$
  
  /**
   * Java example for bisecting k-means clustering.
   */
  public class JavaBisectingKMeansExample {
    public static void main(String[] args) {
      SparkConf sparkConf = new SparkConf().setAppName("JavaBisectingKMeansExample");
      JavaSparkContext sc = new JavaSparkContext(sparkConf);
  
      // $example on$
      ArrayList<Vector> localData = Lists.newArrayList(
        Vectors.dense(0.1, 0.1),   Vectors.dense(0.3, 0.3),
        Vectors.dense(10.1, 10.1), Vectors.dense(10.3, 10.3),
        Vectors.dense(20.1, 20.1), Vectors.dense(20.3, 20.3),
        Vectors.dense(30.1, 30.1), Vectors.dense(30.3, 30.3)
      );
      JavaRDD<Vector> data = sc.parallelize(localData, 2);
  
      BisectingKMeans bkm = new BisectingKMeans()
        .setK(4);
      BisectingKMeansModel model = bkm.run(data);
  
      System.out.println("Compute Cost: " + model.computeCost(data));
  
      Vector[] clusterCenters = model.clusterCenters();
      for (int i = 0; i < clusterCenters.length; i++) {
        Vector clusterCenter = clusterCenters[i];
        System.out.println("Cluster Center " + i + ": " + clusterCenter);
      }
      // $example off$
  
      sc.stop();
    }
  }
  