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linyiqun
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聚类k均值算法实现
聚类k均值算法实现
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‎Clustering/DataMining_KMeans/Client.java‎

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package DataMining_KMeans;
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/**
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* K-means（K均值）算法调用类
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* @author lyq
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*
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*/
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public class Client {
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public static void main(String[] args){
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String filePath = "C:\\Users\\lyq\\Desktop\\icon\\input.txt";
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//聚类中心数量设定
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int classNum = 3;
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KMeansTool tool = new KMeansTool(filePath, classNum);
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tool.kMeansClustering();
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}
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}

‎Clustering/DataMining_KMeans/KMeansTool.java‎

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package DataMining_KMeans;
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import java.io.BufferedReader;
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import java.io.File;
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import java.io.FileReader;
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import java.io.IOException;
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import java.text.MessageFormat;
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import java.util.ArrayList;
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import java.util.Collections;
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/**
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* k均值算法工具类
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*
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* @author lyq
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*
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*/
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public class KMeansTool {
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// 输入数据文件地址
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private String filePath;
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// 分类类别个数
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private int classNum;
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// 类名称
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private ArrayList<String> classNames;
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// 聚类坐标点
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private ArrayList<Point> classPoints;
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// 所有的数据左边点
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private ArrayList<Point> totalPoints;
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public KMeansTool(String filePath, int classNum) {
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this.filePath = filePath;
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this.classNum = classNum;
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readDataFile();
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}
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/**
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* 从文件中读取数据
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*/
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private void readDataFile() {
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File file = new File(filePath);
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ArrayList<String[]> dataArray = new ArrayList<String[]>();
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try {
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BufferedReader in = new BufferedReader(new FileReader(file));
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String str;
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String[] tempArray;
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while ((str = in.readLine()) != null) {
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tempArray = str.split(" ");
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dataArray.add(tempArray);
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}
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in.close();
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} catch (IOException e) {
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e.getStackTrace();
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}
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classPoints = new ArrayList<>();
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totalPoints = new ArrayList<>();
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classNames = new ArrayList<>();
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for (int i = 0, j = 1; i < dataArray.size(); i++) {
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if (j <= classNum) {
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classPoints.add(new Point(dataArray.get(i)[0],
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dataArray.get(i)[1], j + ""));
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classNames.add(i + "");
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j++;
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}
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totalPoints
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.add(new Point(dataArray.get(i)[0], dataArray.get(i)[1]));
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}
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}
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/**
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* K均值聚类算法实现
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*/
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public void kMeansClustering() {
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double tempX = 0;
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double tempY = 0;
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int count = 0;
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double error = Integer.MAX_VALUE;
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Point temp;
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while (error > 0.01 * classNum) {
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for (Point p1 : totalPoints) {
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// 将所有的测试坐标点就近分类
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for (Point p2 : classPoints) {
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p2.computerDistance(p1);
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}
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Collections.sort(classPoints);
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// 取出p1离类坐标点最近的那个点
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p1.setClassName(classPoints.get(0).getClassName());
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}
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error = 0;
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// 按照均值重新划分聚类中心点
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for (Point p1 : classPoints) {
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count = 0;
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tempX = 0;
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tempY = 0;
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for (Point p : totalPoints) {
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if (p.getClassName().equals(p1.getClassName())) {
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count++;
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tempX += p.getX();
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tempY += p.getY();
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}
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}
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tempX /= count;
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tempY /= count;
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error += Math.abs((tempX - p1.getX()));
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error += Math.abs((tempY - p1.getY()));
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// 计算均值
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p1.setX(tempX);
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p1.setY(tempY);
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}
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for (int i = 0; i < classPoints.size(); i++) {
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temp = classPoints.get(i);
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System.out.println(MessageFormat.format("聚类中心点{0}，x={1},y={2}",
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(i + 1), temp.getX(), temp.getY()));
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}
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System.out.println("----------");
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}
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System.out.println("结果值收敛");
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for (int i = 0; i < classPoints.size(); i++) {
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temp = classPoints.get(i);
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System.out.println(MessageFormat.format("聚类中心点{0}，x={1},y={2}",
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(i + 1), temp.getX(), temp.getY()));
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}
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}
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}

‎Clustering/DataMining_KMeans/Point.java‎

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package DataMining_KMeans;
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/**
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* 坐标点类
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*
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* @author lyq
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*
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*/
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public class Point implements Comparable<Point>{
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// 坐标点横坐标
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private double x;
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// 坐标点纵坐标
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private double y;
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//以此点作为聚类中心的类的类名称
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private String className;
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// 坐标点之间的欧式距离
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private Double distance;
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public Point(double x, double y) {
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this.x = x;
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this.y = y;
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}
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public Point(String x, String y) {
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this.x = Double.parseDouble(x);
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this.y = Double.parseDouble(y);
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}
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public Point(String x, String y, String className) {
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this.x = Double.parseDouble(x);
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this.y = Double.parseDouble(y);
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this.className = className;
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}
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/**
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* 距离目标点p的欧几里得距离
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*
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* @param p
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*/
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public void computerDistance(Point p) {
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if (p == null) {
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return;
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}
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this.distance = (this.x - p.x) * (this.x - p.x) + (this.y - p.y)
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* (this.y - p.y);
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}
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public double getX() {
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return x;
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}
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public void setX(double x) {
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this.x = x;
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}
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public double getY() {
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return y;
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}
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public void setY(double y) {
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this.y = y;
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}
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public String getClassName() {
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return className;
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}
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public void setClassName(String className) {
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this.className = className;
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}
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public double getDistance() {
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return distance;
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}
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public void setDistance(double distance) {
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this.distance = distance;
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}
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@Override
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public int compareTo(Point o) {
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// TODO Auto-generated method stub
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return this.distance.compareTo(o.distance);
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}
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}

‎Clustering/DataMining_KMeans/input.txt‎

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3 3
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4 10
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9 6
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14 8
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18 11
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21 7

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