发布时间：2023-12-19 19:30
package com.wxshi.kmean;
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.List;
/**
* Kmeans算法演示
* @author wxshi
*
*/
public class Kmeans {
private List> centers;
private List> newCenters;
private List>> clusterList;
private int clusterNum = 5; //默认聚类的个数
/**
* 默认构造不对外抛
*/
private Kmeans(){
}
public Kmeans(int clusterNum){
if(clusterNum<=0){
clusterNum = 5;
}
this.clusterNum = clusterNum;
centers = new ArrayList>();
newCenters = new ArrayList>();
clusterList = new ArrayList>>();
}
/**
* 初始化簇，开始为空
* @param args
* @throws IOException
*/
public List>> initclusterList() {
clusterList = new ArrayList>>();
for (int i = 0; i < clusterNum; i++) {
clusterList.add(new ArrayList>());
}
return clusterList;
}
/**
* 初始化聚类中心节点，随机选择
* 这里随便选择几个
* @param dataList
*/
private void initCenters(List> dataList){
for (int i = 0; i < clusterNum; i++) {
centers.add(dataList.get(i + 2));
clusterList.add(new ArrayList>());
}
}
/**
* 新旧中心切换
* 清空原来的簇中数据，重新放置数据
*/
private void replaceCenters() {
centers = new ArrayList>(newCenters);
newCenters = new ArrayList>();
initclusterList();
}
/**
* 欧式距离计算
* @param element1
* @param element2
* @return
*/
private double distance(double element1,double element2){
double distance = 0;
distance = ((element1 - element2) / (element1 + element2)) * ((element1 - element2) / (element1 + element2));
return distance;
}
/**
* 新旧聚类中心距离计算
* @return
*/
private double distanceOfCenters() {
// 计算新旧中心之间的距离，当距离小于阈值时，聚类算法结束
double distance = 0;
for (int i = 0; i < clusterNum; i++) {
for (int j = 0; j < centers.get(i).size(); j++) {// 计算两点之间的距离
distance += distance(centers.get(i).get(j) , newCenters.get(i).get(j));
}
}
return distance;
}
/**
* 重新计算聚类中心
*/
private void newCenters() {
for (int i = 0; i < clusterNum; i++) {
int len = clusterList.get(i).size();
ArrayList tmpList = new ArrayList();
for (int j = 0; j < centers.get(0).size(); j++) {
double sum = 0;
for (int t = 0; t < len; t++) {
sum += clusterList.get(i).get(t).get(j);
}
tmpList.add(sum / len);
}
newCenters.add(tmpList);
}
}
/**
* 核心方法
* 迭代簇，将距离最近的节点加入簇
* @param dataList
*/
private void intoCuster(List> dataList){
for (int i = 0; i < dataList.size(); i++) {
double minDistance = 99999999;
int centerIndex = -1;
for (int j = 0; j < clusterNum; j++) {// 计算最近距离
double currentDistance = 0;
for (int t = 0; t < centers.get(j).size(); t++) {// 计算两点之间的距离
currentDistance += distance(centers.get(j).get(t) , dataList.get(i).get(t)) ;
}
if (minDistance > currentDistance) {
minDistance = currentDistance;
centerIndex = j;
}
}
clusterList.get(centerIndex).add(dataList.get(i));
}
}
/**
* 读取文件,获取数据
* @param dir
* @return
*/
public List> readFile(String dir) {
List> dataList = new ArrayList>();
try {
BufferedReader br = new BufferedReader(new InputStreamReader(new FileInputStream(\"wine.txt\")));
String data = null;
while ((data = br.readLine()) != null) {
String[] fields = data.split(\",\");
List tmpList = new ArrayList();
for (int i = 0; i < fields.length; i++) {
tmpList.add(Double.parseDouble(fields[i]));
}
dataList.add((ArrayList) tmpList);
}
br.close();
} catch (IOException e) {
e.printStackTrace();
}
return dataList;
}
/**
* 打印结果
*/
private void print() {
for (int i = 0; i < clusterNum; i++) {
System.out.println(\"\\nCluster: \" + (i + 1) + \" size: \" + clusterList.get(i).size() + \" :\\n\");
for (int j = 0; j < clusterList.get(i).size(); j++) {
System.out.println(clusterList.get(i).get(j));
}
}
}
/**
* @param args
* @throws IOException
*/
public static void main(String[] args) throws IOException {
Kmeans kmeans = new Kmeans(5);
// 读入原始数据
List> dataList = kmeans.readFile(\"wine.txt\");
// 随机确定K个初始聚类中心
kmeans.initCenters(dataList);
// 进行若干次迭代，直到聚类中心稳定
while (true) {
kmeans.intoCuster(dataList);
kmeans.newCenters();
double distance = kmeans.distanceOfCenters();
// 小于阈值时，结束循环
if (distance == 0) {
break;
}
// 否则，新的中心来代替旧的中心，进行下一轮迭代
else {
kmeans.replaceCenters();
}
}
kmeans.print();
}
} import java.util.ArrayList;
import java.util.Random;
/**
* K均值聚类算法
*/
public class Kmeans2 {
private int k;// 分成多少簇
private int m;// 迭代次数
private int dataSetLength;// 数据集元素个数，即数据集的长度
private ArrayList dataSet;// 数据集链表
private ArrayList center;// 中心链表
private ArrayList> cluster; // 簇
private ArrayList jc;// 误差平方和，k越接近dataSetLength，误差越小
private Random random;
/**
* 设置需分组的原始数据集
* @param dataSet
*/
public void setDataSet(ArrayList dataSet) {
this.dataSet = dataSet;
}
/**
* 获取结果分组
* @return 结果集
*/
public ArrayList> getCluster() {
return cluster;
}
/**
* 构造函数，传入需要分成的簇数量
* @param k
* 簇数量,若k<=0时，设置为1，若k大于数据源的长度时，置为数据源的长度
*/
public Kmeans2(int k) {
if (k <= 0) {
k = 1;
}
this.k = k;
}
/**
* 初始化
*/
private void init() {
m = 0;
random = new Random();
if (dataSet == null || dataSet.size() == 0) {
initDataSet();
}
dataSetLength = dataSet.size();
if (k > dataSetLength) {
k = dataSetLength;
}
center = initCenters();
cluster = initCluster();
jc = new ArrayList();
}
/**
* 如果调用者未初始化数据集，则采用内部测试数据集
*/
private void initDataSet() {
dataSet = new ArrayList();
// 其中{6,3}是一样的，所以长度为15的数据集分成14簇和15簇的误差都为0
float[][] dataSetArray = new float[][] { { 8, 2 }, { 3, 4 }, { 2, 5 },
{ 4, 2 }, { 7, 3 }, { 6, 2 }, { 4, 7 }, { 6, 3 }, { 5, 3 },
{ 6, 3 }, { 6, 9 }, { 1, 6 }, { 3, 9 }, { 4, 1 }, { 8, 6 } };
for (int i = 0; i < dataSetArray.length; i++) {
dataSet.add(dataSetArray[i]);
}
}
/**
* 初始化中心数据链表，分成多少簇就有多少个中心点
*
* @return 中心点集
*/
private ArrayList initCenters() {
ArrayList center = new ArrayList();
int[] randoms = new int[k];
boolean flag;
int temp = random.nextInt(dataSetLength);
randoms[0] = temp;
for (int i = 1; i < k; i++) {
flag = true;
while (flag) {
temp = random.nextInt(dataSetLength);
int j = 0;
while (j < i) {
if (temp == randoms[j]) {
break;
}
j++;
}
if (j == i) {
flag = false;
}
}
randoms[i] = temp;
}
for (int i = 0; i < k; i++) {
center.add(dataSet.get(randoms[i]));// 生成初始化中心链表
}
return center;
}
/**
* 初始化簇集合
*
* @return 一个分为k簇的空数据的簇集合
*/
private ArrayList> initCluster() {
ArrayList> cluster = new ArrayList>();
for (int i = 0; i < k; i++) {
cluster.add(new ArrayList());
}
return cluster;
}
/**
* 计算两个点之间的距离
*
* @param element
* 点1
* @param center
* 点2
* @return 距离
*/
private float distance(float[] element, float[] center) {
float distance = 0.0f;
float x = element[0] - center[0];
float y = element[1] - center[1];
float z = x * x + y * y;
distance = (float) Math.sqrt(z);
return distance;
}
/**
* 获取距离集合中最小距离的位置
*
* @param distance
* 距离数组
* @return 最小距离在距离数组中的位置
*/
private int minDistance(float[] distance) {
float minDistance = distance[0];
int minLocation = 0;
for (int i = 1; i < distance.length; i++) {
if (distance[i] < minDistance) {
minDistance = distance[i];
minLocation = i;
} else if (distance[i] == minDistance) // 如果相等，随机返回一个位置
{
if (random.nextInt(10) < 5) {
minLocation = i;
}
}
}
return minLocation;
}
/**
* 核心，将当前元素放到最小距离中心相关的簇中
*/
private void clusterSet() {
float[] distance = new float[k];
for (int i = 0; i < dataSetLength; i++) {
for (int j = 0; j < k; j++) {
distance[j] = distance(dataSet.get(i), center.get(j));
}
int minLocation = minDistance(distance);
cluster.get(minLocation).add(dataSet.get(i));// 核心，将当前元素放到最小距离中心相关的簇中
}
}
/**
* 求两点误差平方的方法
*
* @param element
* 点1
* @param center
* 点2
* @return 误差平方
*/
private float errorSquare(float[] element, float[] center) {
float x = element[0] - center[0];
float y = element[1] - center[1];
float errSquare = x * x + y * y;
return errSquare;
}
/**
* 计算误差平方和准则函数方法
*/
private void countRule() {
float jcF = 0;
for (int i = 0; i < cluster.size(); i++) {
for (int j = 0; j < cluster.get(i).size(); j++) {
jcF += errorSquare(cluster.get(i).get(j), center.get(i));
}
}
jc.add(jcF);
}
/**
* 设置新的簇中心方法
*/
private void setNewCenter() {
for (int i = 0; i < k; i++) {
int n = cluster.get(i).size();
if (n != 0) {
float[] newCenter = { 0, 0 };
for (int j = 0; j < n; j++) {
newCenter[0] += cluster.get(i).get(j)[0];
newCenter[1] += cluster.get(i).get(j)[1];
}
// 设置一个平均值
newCenter[0] = newCenter[0] / n;
newCenter[1] = newCenter[1] / n;
center.set(i, newCenter);
}
}
}
/**
* 打印数据，测试用
* @param dataArray
* 数据集
* @param dataArrayName
* 数据集名称
*/
public void printDataArray(ArrayList dataArray, String dataArrayName) {
for (int i = 0; i < dataArray.size(); i++) {
System.out.println(\"print:\" + dataArrayName + \"[\" + i + \"]={\" + dataArray.get(i)[0] + \",\" + dataArray.get(i)[1] + \"}\");
}
System.out.println(\"===================================\");
}
/**
* Kmeans算法核心过程方法
*/
private void kmeans() {
init();
// 循环分组，直到误差不变为止
while (true) {
clusterSet();
countRule();
// 误差不变了，分组完成
if (m != 0) {
if (jc.get(m) - jc.get(m - 1) == 0) {
break;
}
}
setNewCenter();
m++;
cluster.clear();
cluster = initCluster();
}
}
/**
* 执行算法
*/
public void execute() {
long startTime = System.currentTimeMillis();
System.out.println(\"kmeans begins\");
kmeans();
long endTime = System.currentTimeMillis();
System.out.println(\"kmeans running time=\" + (endTime - startTime) + \"ms\");
System.out.println(\"kmeans ends\");
System.out.println();
}
public static void main(String[] args){
//初始化一个Kmean对象，将k置为10
Kmeans2 k=new Kmeans2(4);
ArrayList dataSet=new ArrayList();
dataSet.add(new float[]{1,22});
dataSet.add(new float[]{3,333});
dataSet.add(new float[]{3,4});
dataSet.add(new float[]{5,6});
dataSet.add(new float[]{8,9999});
dataSet.add(new float[]{4,5});
dataSet.add(new float[]{6,4});
dataSet.add(new float[]{3,95});
dataSet.add(new float[]{5,9});
dataSet.add(new float[]{4,7777});
dataSet.add(new float[]{1,9});
dataSet.add(new float[]{7,844});
//设置原始数据集
k.setDataSet(dataSet);
//执行算法
k.execute();
//得到聚类结果
ArrayList> cluster=k.getCluster();
//查看结果
for(int i=0;i