在Java实现的聚类算法中，以地理位置经度、纬度聚类为基础，增加时间属性和配送量属性的权重，以达到相对均衡的聚类效果，请以JAVA代码实现？

可以使用K-means算法实现地理位置经度、纬度聚类，并增加时间属性和配送量属性的权重。以下是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 = ; 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) {
// 清空聚类结果
for (Cluster cluster : clusters) {
cluster.clearPoints();
}

// 将每个数据点分配到最近的聚类中心
for (Point point : points) {
Cluster nearestCluster = null;
double minDistance = Double.MAX_VALUE;
for (Cluster cluster : clusters) {
double distance = cluster.getDistance(point);
if (distance < minDistance) {
nearestCluster = cluster;
minDistance = distance;
}
}
nearestCluster.addPoint(point);
}

// 更新聚类中心
changed = false;
for (Cluster cluster : clusters) {
if (cluster.updateCenter()) {
changed = true;
}
}
}
}

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

public static void main(String[] args) {
// 生成测试数据
List<Point> points = new ArrayList<>();
points.add(new Point(116.403963, 39.915119, 1, 10));
points.add(new Point(116.404289, 39.9146, 2, 20));
points.add(new Point(116.404756, 39.914062, 3, 30));
points.add(new Point(116.405282, 39.913569, 4, 40));
points.add(new Point(116.405862, 39.913131, 5, 50));
points.add(new Point(116.406488, 39.912758, 6, 60));
points.add(new Point(116.407148, 39.91246, 7, 70));
points.add(new Point(116.407831, 39.912246, 8, 80));
points.add(new Point(116.408524, 39.912123, 9, 90));
points.add(new Point(116.409214, 39.912094, 10, 100));

// 聚类
KMeans kMeans = new KMeans(3, points);
kMeans.cluster();

// 输出聚类结果
List<Cluster> clusters = kMeans.getClusters();
for (int i = ; i < clusters.size(); i++) {
Cluster cluster = clusters.get(i);
System.out.println("Cluster " + (i + 1) + ":");
for (Point point : cluster.getPoints()) {
System.out.println(point);
}
}
}
}

class Point {
private double longitude; // 经度
private double latitude; // 纬度
private double time; // 时间
private double quantity; // 配送量

public Point(double longitude, double latitude, double time, double quantity) {
this.longitude = longitude;
this.latitude = latitude;
this.time = time;
this.quantity = quantity;
}

public double getLongitude() {
return longitude;
}

public double getLatitude() {
return latitude;
}

public double getTime() {
return time;
}

public double getQuantity() {
return quantity;
}

@Override
public String toString() {
return "(" + longitude + ", " + latitude + ", " + time + ", " + quantity + ")";
}
}

class Cluster {
private Point center; // 聚类中心
private List<Point> points; // 聚类结果

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

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

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

public boolean updateCenter() {
if (points.isEmpty()) {
return false;
}

double sumLongitude = ;
double sumLatitude = ;
double sumTime = ;
double sumQuantity = ;
for (Point point : points) {
sumLongitude += point.getLongitude();
sumLatitude += point.getLatitude();
sumTime += point.getTime();
sumQuantity += point.getQuantity();
}
double newLongitude = sumLongitude / points.size();
double newLatitude = sumLatitude / points.size();
double newTime = sumTime / points.size();
double newQuantity = sumQuantity / points.size();
if (newLongitude == center.getLongitude() && newLatitude == center.getLatitude() && newTime == center.getTime() && newQuantity == center.getQuantity()) {
return false;
} else {
center = new Point(newLongitude, newLatitude, newTime, newQuantity);
return true;
}
}

public List<Point> getPoints() {
return points;
}

public double getDistance(Point point) {
double distanceLongitude = center.getLongitude() - point.getLongitude();
double distanceLatitude = center.getLatitude() - point.getLatitude();
double distanceTime = center.getTime() - point.getTime();
double distanceQuantity = center.getQuantity() - point.getQuantity();
return Math.sqrt(distanceLongitude * distanceLongitude + distanceLatitude * distanceLatitude + distanceTime * distanceTime + distanceQuantity * distanceQuantity);
}
}