<strong><span style="font-size:18px;">/***
* @author YangXin
* @info 利用点集测试K-Means聚类算法
*/
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
在内存中执行k-means聚类算法
最新推荐文章于 2023-04-05 11:32:52 发布