in-memory形式的牧户K-Means聚类

<strong><span style="font-size:18px;">/***
 * @author YangXin
 * @info 以in-memory形式的模糊k-means聚类示例
 */
package unitNine;

import java.util.ArrayList;
import java.util.List;

import org.apache.mahout.clustering.fuzzykmeans.FuzzyKMeansClusterer;
import org.apache.mahout.clustering.fuzzykmeans.SoftCluster;
import org.apache.mahout.common.distance.EuclideanDistanceMeasure;
import org.apache.mahout.math.Vector;

public class FuzzyKMeansExample {
	public static void main(){
		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<SoftCluster> clusters = new ArrayList<SoftCluster>();
		int clusterId = 0;
		for(Vector v : randomPoints){
			clusters.add(new SoftCluster(v, clusterId++, new EuclideanDistanceMeasure()));
		}
		
		List<List<SoftCluster>> finalClusters = FuzzyKMeansClusterer.clusterPoints(sampleData, clusters, new EuclideanDistanceMeasure(), 0.01, 3, 10);
		for(SoftCluster cluster : finalClusters.get(finalClusters.size() - 1)){
				System.out.println("Fuzzy Cluster id : " + cluster.getId() + " center:" + cluster.getCenter().asFormatString());
		}
	}
}
</span></strong>