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.ml;
  
  // $example on$
  import org.apache.spark.ml.clustering.BisectingKMeans;
  import org.apache.spark.ml.clustering.BisectingKMeansModel;
  import org.apache.spark.ml.linalg.Vector;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  // $example off$
  import org.apache.spark.sql.SparkSession;
  
  
  /**
   * An example demonstrating bisecting k-means clustering.
   * Run with
   * <pre>
   * bin/run-example ml.JavaBisectingKMeansExample
   * </pre>
   */
  public class JavaBisectingKMeansExample {
  
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaBisectingKMeansExample")
        .getOrCreate();
  
      // $example on$
      // Loads data.
      Dataset<Row> dataset = spark.read().format("libsvm").load("data/mllib/sample_kmeans_data.txt");
  
      // Trains a bisecting k-means model.
      BisectingKMeans bkm = new BisectingKMeans().setK(2).setSeed(1);
      BisectingKMeansModel model = bkm.fit(dataset);
  
      // Evaluate clustering.
      double cost = model.computeCost(dataset);
      System.out.println("Within Set Sum of Squared Errors = " + cost);
  
      // Shows the result.
      System.out.println("Cluster Centers: ");
      Vector[] centers = model.clusterCenters();
      for (Vector center : centers) {
        System.out.println(center);
      }
      // $example off$
  
      spark.stop();
    }
  }
  