在內存中執行k-means聚類算法

來自： http://blog.csdn.net/u012965373/article/details/50754381

/***
 * @author YangXin
 * @info 利用點集測試K-Means聚類算法
 */
package unitNine;
import java.util.ArrayList;
import java.util.List;
import org.apache.mahout.clustering.Cluster;
import org.apache.mahout.clustering.UncommonDistributions;
import org.apache.mahout.common.distance.EuclideanDistanceMeasure;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Vector;
public class KMeansExample {
    private static void generateSamples(List<Vector> vectors, int num, double mx, double my, double sd){
        for(int i = 0; i < num; i++){
            vectors.add(new DenseVector(new double[]{UncommonDistributions.rNorm(mx, sd),UncommonDistributions.rNorm(my, sd) }));
        }
    }

    public static void main(String[] args){
        List<Vector> sampleData = new ArrayList<Vector>();

        RandomPointsUtil.generateSamples(sampleData, 400, 1, 1, 3);
        RandomPointsUtil.generateSamples(sampleData, 300, 1, 0, 0.5);
        RandomPointsUtil.generateSamples(sampleData, 300, 0, 2, 0.1);

        int k = 3;
        List<Vector> randomPoints = RandomPointsUtil.chooseRandomPoints(
                sampleData, k);
        List<Cluster> clusters = new ArrayList<Cluster>();

        int clusterId = 0;
        for (Vector v : randomPoints) {
            clusters.add(new Cluster(v, clusterId++, new EuclideanDistanceMeasure()));
        }

        List<List<Cluster>> finalClusters = KMeansClusterer.clusterPoints(
                sampleData, clusters, new EuclideanDistanceMeasure(), 3, 0.01);
        for (Cluster cluster : finalClusters.get(finalClusters.size() - 1)) {
            System.out.println("Cluster id: " + cluster.getId() + " center: "
                    + cluster.getCenter().asFormatString());
        }
}
</span></strong>

<strong><span style="font-size:18px;">/***
 * @author YangXin
 * @info 處理隨機點的類
 */
package unitNine;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Vector;
public class RandomPointsUtil {
    public static void generateSamples(List<Vector> vectors, int num,
            double mx, double my, double sd) {
        for (int i = 0; i < num; i++) {
            vectors.add(new DenseVector(new double[] {
                    org.apache.mahout.clustering.UncommonDistributions.rNorm(mx, sd),
                    org.apache.mahout.clustering.UncommonDistributions.rNorm(my, sd) }));
        }
    }   

  public static List<Vector> chooseRandomPoints(Iterable<Vector> vectors, int k) {
    List<Vector> chosenPoints = new ArrayList<Vector>(k);
    Random random = RandomUtils.getRandom();
    for (Vector value : vectors) {
      int currentSize = chosenPoints.size();
      if (currentSize < k) {
        chosenPoints.add(value);
      } else if (random.nextInt(currentSize + 1) == 0) { // with chance 1/(currentSize+1) pick new element
        int indexToRemove = random.nextInt(currentSize); // evict one chosen randomly
        chosenPoints.remove(indexToRemove);
        chosenPoints.add(value);
      }
    }
    return chosenPoints;
  }
}