聚类算法kmeans java 代码

我可以回答这个问题。K-means算法是一种常用的聚类算法，可以用Java语言实现。以下是一个简单的K-means Java代码示例：

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

public class KMeans {
    private int k; // 聚类数
    private List<Point> points; // 数据点
    private List<Cluster> clusters; // 聚类结果

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

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

        // 迭代聚类
        boolean changed = true;
        while (changed) {
            changed = false;
            // 清空聚类结果
            for (Cluster cluster : clusters) {
                cluster.clear();
            }
            // 将每个数据点归入最近的聚类中心
            for (Point point : points) {
                Cluster nearestCluster = null;
                double minDistance = Double.MAX_VALUE;
                for (Cluster cluster : clusters) {
                    double distance = point.distanceTo(cluster.getCenter());
                    if (distance < minDistance) {
                        minDistance = distance;
                        nearestCluster = cluster;
                    }
                }
                nearestCluster.addPoint(point);
            }
            // 更新聚类中心
            for (Cluster cluster : clusters) {
                if (cluster.updateCenter()) {
                    changed = true;
                }
            }
        }
    }

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

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.x;
        double dy = y - other.y;
        return Math.sqrt(dx * dx + dy * dy);
    }
}

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

    public Cluster(Point center) {
        this.center = center;
        this.points = new ArrayList<>();
    }

    public Point getCenter() {
        return center;
    }

    public void addPoint(Point point) {
        points.add(point);
    }

    public void clear() {
        points.clear();
    }

    public boolean updateCenter() {
        if (points.isEmpty()) {
            return false;
        }
        double sumX = 0;
        double sumY = 0;
        for (Point point : points) {
            sumX += point.getX();
            sumY += point.getY();
        }
        double newCenterX = sumX / points.size();
        double newCenterY = sumY / points.size();
        if (newCenterX == center.getX() && newCenterY == center.getY()) {
            return false;
        }
        center = new Point(newCenterX, newCenterY);
        return true;
    }
}

这段代码实现了K-means算法的基本流程，包括随机初始化聚类中心、迭代聚类、更新聚类中心等。你可以根据自己的需求进行修改和扩展。