K-means距离 - Java

以下是使用Java实现K-means聚类算法的示例代码：

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

public class KMeans {
    private int k; // 聚类簇的数量
    private List<Point> points; // 数据点列表
    private List<Cluster> clusters; // 聚类簇列表

    public KMeans(int k, List<Point> points) {
        this.k = k;
        this.points = points;
        this.clusters = new ArrayList<>();
    }

    public void run() {
        // 随机初始化聚类簇中心点
        Random random = new Random();
        for (int i = 0; i < k; i++) {
            Point center = points.get(random.nextInt(points.size()));
            Cluster cluster = new Cluster(i, center);
            clusters.add(cluster);
        }

        boolean converged = false;
        int iteration = 0;

        while (!converged) {
            clearClusters();
            List<Point> prevCenters = getCenters();

            assignPointsToClusters();
            calculateCenters();

            iteration++;

            // 判断是否收敛
            List<Point> currentCenters = getCenters();
            converged = true;
            for (int i = 0; i < k; i++) {
                if (!currentCenters.get(i).equals(prevCenters.get(i))) {
                    converged = false;
                    break;
                }
            }

            if (converged) {
                System.out.println("Algorithm converged after " + iteration + " iterations");
            }
        }
    }

    private void clearClusters() {
        for (Cluster cluster : clusters) {
            cluster.clearPoints();
        }
    }

    private List<Point> getCenters() {
        List<Point> centers = new ArrayList<>();
        for (Cluster cluster : clusters) {
            Point center = cluster.getCenter();
            centers.add(center);
        }
        return centers;
    }

    private void assignPointsToClusters() {
        for (Point point : points) {
            double minDistance = Double.MAX_VALUE;
            int clusterId = -1;

            for (Cluster cluster : clusters) {
                double distance = point.distanceTo(cluster.getCenter());
                if (distance < minDistance) {
                    minDistance = distance;
                    clusterId = cluster.getId();
                }
            }

            clusters.get(clusterId).addPoint(point);
        }
    }

    private void calculateCenters() {
        for (Cluster cluster : clusters) {
            List<Point> points = cluster.getPoints();
            double sumX = 0;
            double sumY = 0;

            for (Point point : points) {
                sumX += point.getX();
                sumY += point.getY();
            }

            double centerX = sumX / points.size();
            double centerY = sumY / points.size();

            cluster.setCenter(new Point(centerX, centerY));
        }
    }

    public List<Cluster> getClusters() {
        return clusters;
    }

    public static void main(String[] args) {
        // 生成一些随机数据点
        List<Point> points = new ArrayList<>();
        points.add(new Point(2, 10));
        points.add(new Point(2, 5));
        points.add(new Point(8, 4));
        points.add(new Point(5, 8));
        points.add(new Point(7, 5));
        points.add(new Point(6, 4));
        points.add(new Point(1, 2));
        points.add(new Point(4, 9));

        KMeans kMeans = new KMeans(2, points);
        kMeans.run();

        List<Cluster> clusters = kMeans.getClusters();
        for (Cluster cluster : clusters) {
            System.out.println("Cluster " + cluster.getId() + " center: " + cluster.getCenter());
            System.out.println("Points in cluster " + cluster.getId() + ": " + cluster.getPoints());
            System.out.println();
        }
    }
}

class Point {
    private double x;
    private double y;

    public Point(double x, double y) {
        this.x = x;
        this.y = y;
    }

    public double getX() {
        return x;
    }

    public double getY() {
        return y;
    }

    public double distanceTo(Point other) {
        double dx = x - other.getX();
        double dy = y - other.getY();
        return Math.sqrt(dx * dx + dy * dy);
    }

    @Override
    public String toString() {
        return "(" + x + ", " + y + ")";
    }
}

class Cluster {
    private int id;
    private Point center;
    private List<Point> points;

    public Cluster(int id,