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>