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关于K-Means介绍很多，还不清楚可以查一些相关资料。
个人对其实现步骤简单总结为4步:
1.选出k值,随机出k个起始质心点。
2.分别计算每个点和k个起始质点之间的距离,就近归类。
3.最终中心点集可以划分为k类,分别计算每类中新的中心点。
4.重复2,3步骤对所有点进行归类,如果当所有分类的质心点不再改变,则最终收敛。
下面贴代码。
1.入口类，基本读取数据源进行训练然后输出。 数据源文件和源码后面会补上。
package com.hyr.kmeans;import .bytecode.opencsv.CSVReader;import java.io.FileReader;import java.io.FileWriter;import java.io.IOException;import java.util.ArrayList;import java.util.List;public class KmeansMain {public static void main(String[] args) throws IOException {// 读取数据源文件CSVReader reader = new CSVReader(new FileReader("src/main/resources/data.csv")); // 数据源FileWriter writer = new FileWriter("src/main/resources/out.csv");List<String[]> myEntries = reader.readAll(); // 6.8, 12.6// 转换数据点集List<Point> points = new ArrayList<Point>(); // 数据点集for (String[] entry : myEntries) {points.add(new Point(Float.parseFloat(entry[0]), Float.parseFloat(entry[1])));}int k = 6; // K值int type = 1;KmeansModel model = Kmeans.run(points, k, type);writer.write("==================== K is " + model.getK() + " , Object Funcion Value is " + model.getOfv() + " , calc_distance_type is " + model.getCalc_distance_type() + " ====================\n");int i = 0;for (Cluster cluster : model.getClusters()) {i++;writer.write("==================== classification " + i + " ====================\n");for (Point point : cluster.getPoints()) {writer.write(point.toString() + "\n");}writer.write("\n");writer.write("centroid is " + cluster.getCentroid().toString());writer.write("\n\n");}writer.close();}}
2.最终生成的模型类,也就是最终训练好的结果。K值,计算的点距离类型以及object function value值。
package com.hyr.kmeans;import java.util.ArrayList;import java.util.List;public class KmeansModel {private List<Cluster> clusters = new ArrayList<Cluster>();private Double ofv;private int k; // k值private int calc_distance_type;public KmeansModel(List<Cluster> clusters, Double ofv, int k, int calc_distance_type) {this.clusters = clusters;this.ofv = ofv;this.k = k;this.calc_distance_type = calc_distance_type;}public List<Cluster> getClusters() {return clusters;}public Double getOfv() {return ofv;}public int getK() {return k;}public int getCalc_distance_type() {return calc_distance_type;}}
3.数据集点对象,包含点的维度,代码里只给出了x轴,y轴二维。以及点的距离计算。通过类型选择距离公式。给出了几种常用的距离公式。
package com.hyr.kmeans;public class Point {private Float x;// x 轴private Float y; // y 轴public Point(Float x, Float y) {this.x = x;this.y = y;}public Float getX() {return x;}public void setX(Float x) {this.x = x;}public Float getY() {return y;}public void setY(Float y) {this.y = y;}@Overridepublic String toString() {return "Point{" +"x=" + x +", y=" + y +'}';}/*** 计算距离** @param centroid 质心点* @param type* @return*/public Double calculateDistance(Point centroid, int type) {// TODODouble result = null;switch (type) {case 1:result = calcL1Distance(centroid);break;case 2:result = calcCanberraDistance(centroid);break;case 3:result = calcEuclidianDistance(centroid);break;}return result;}/*计算距离公式*/private Double calcL1Distance(Point centroid) {double res = 0;res = Math.abs(getX() - centroid.getX()) + Math.abs(getY() - centroid.getY());return res / (double) 2;}private double calcEuclidianDistance(Point centroid) {return Math.sqrt(Math.pow((centroid.getX() - getX()), 2) + Math.pow((centroid.getY() - getY()), 2));}private double calcCanberraDistance(Point centroid) {double res = 0;res = Math.abs(getX() - centroid.getX()) / (Math.abs(getX()) + Math.abs(centroid.getX()))+ Math.abs(getY() - centroid.getY()) / (Math.abs(getY()) + Math.abs(centroid.getY()));return res / (double) 2;}@Overridepublic boolean equals(Object obj) {Point other = (Point) obj;if (getX().equals(other.getX()) && getY().equals(other.getY())) {return true;}return false;}}
4.训练后最终得到的分类。包含该分类的质点,属于该分类的点集合该分类是否收敛。
package com.hyr.kmeans;import java.util.ArrayList;import java.util.List;public class Cluster {private List<Point> points = new ArrayList<Point>(); // 属于该分类的点集private Point centroid; // 该分类的中心质点private boolean isConvergence = false;public Point getCentroid() {return centroid;}public void setCentroid(Point centroid) {this.centroid = centroid;}@Overridepublic String toString() {return centroid.toString();}public List<Point> getPoints() {return points;}public void setPoints(List<Point> points) {this.points = points;}public void initPoint() {points.clear();}public boolean isConvergence() {return isConvergence;}public void setConvergence(boolean convergence) {isConvergence = convergence;}}
5.K-Meams训练类。按照上面所说四个步骤不断进行训练。
package com.hyr.kmeans;import java.util.ArrayList;import java.util.List;import java.util.Random;public class Kmeans {/*** kmeans** @param points 数据集* @param kK值* @param k计算距离方式*/public static KmeansModel run(List<Point> points, int k, int type) {// 初始化质心点List<Cluster> clusters = initCentroides(points, k);while (!checkConvergence(clusters)) { // 所有分类是否全部收敛// 1.计算距离对每个点进行分类// 2.判断质心点是否改变,未改变则该分类已经收敛// 3.重新生成质心点initClusters(clusters); // 重置分类中的点classifyPoint(points, clusters, type);// 计算距离进行分类recalcularCentroides(clusters); // 重新计算质心点}// 计算目标函数值Double ofv = calcularObjetiFuncionValue(clusters);KmeansModel kmeansModel = new KmeansModel(clusters, ofv, k, type);return kmeansModel;}/*** 初始化k个质心点** @param points 点集* @param kK值* @return 分类集合对象*/private static List<Cluster> initCentroides(List<Point> points, Integer k) {List<Cluster> centroides = new ArrayList<Cluster>();// 求出数据集的范围(找出所有点的x最小、最大和y最小、最大坐标。)Float max_X = Float.NEGATIVE_INFINITY;Float max_Y = Float.NEGATIVE_INFINITY;Float min_X = Float.POSITIVE_INFINITY;Float min_Y = Float.POSITIVE_INFINITY;for (Point point : points) {max_X = max_X < point.getX() ? point.getX() : max_X;max_Y = max_Y < point.getY() ? point.getY() : max_Y;min_X = min_X > point.getX() ? point.getX() : min_X;min_Y = min_Y > point.getY() ? point.getY() : min_Y;}System.out.println("min_X" + min_X + ",max_X:" + max_X + ",min_Y" + min_Y + ",max_Y" + max_Y);// 在范围内随机初始化k个质心点Random random = new Random();// 随机初始化k个中心点for (int i = 0; i < k; i++) {float x = random.nextFloat() * (max_X - min_X) + min_X;float y = random.nextFloat() * (max_Y - min_Y) + min_X;Cluster c = new Cluster();Point centroide = new Point(x, y); // 初始化的随机中心点c.setCentroid(centroide);centroides.add(c);}return centroides;}/*** 重新计算质心点** @param clusters*/private static void recalcularCentroides(List<Cluster> clusters) {for (Cluster c : clusters) {if (c.getPoints().isEmpty()) {c.setConvergence(true);continue;}// 求均值,作为新的质心点Float x;Float y;Float sum_x = 0f;Float sum_y = 0f;for (Point point : c.getPoints()) {sum_x += point.getX();sum_y += point.getY();}x = sum_x / c.getPoints().size();y = sum_y / c.getPoints().size();Point nuevoCentroide = new Point(x, y); // 新的质心点if (nuevoCentroide.equals(c.getCentroid())) { // 如果质心点不再改变 则该分类已经收敛c.setConvergence(true);} else {c.setCentroid(nuevoCentroide);}}}/*** 计算距离,对点集进行分类** @param points 点集* @param clusters 分类* @param type计算距离方式*/private static void classifyPoint(List<Point> points, List<Cluster> clusters, int type) {for (Point point : points) {Cluster masCercano = clusters.get(0); // 该点计算距离后所属的分类Double minDistancia = Double.MAX_VALUE; // 最小距离for (Cluster cluster : clusters) {Double distancia = point.calculateDistance(cluster.getCentroid(), type); // 点和每个分类质心点的距离if (minDistancia > distancia) { // 得到该点和k个质心点最小的距离minDistancia = distancia;masCercano = cluster; // 得到该点的分类}}masCercano.getPoints().add(point); // 将该点添加到距离最近的分类中}}private static void initClusters(List<Cluster> clusters) {for (Cluster cluster : clusters) {cluster.initPoint();}}/*** 检查收敛** @param clusters* @return*/private static boolean checkConvergence(List<Cluster> clusters) {for (Cluster cluster : clusters) {if (!cluster.isConvergence()) {return false;}}return true;}/*** 计算目标函数值** @param clusters* @return*/private static Double calcularObjetiFuncionValue(List<Cluster> clusters) {Double ofv = 0d;for (Cluster cluster : clusters) {for (Point point : cluster.getPoints()) {int type = 1;ofv += point.calculateDistance(cluster.getCentroid(), type);}}return ofv;}}
最终训练结果:
==================== K is 6 , Object Funcion Value is 21.82857036590576 , calc_distance_type is 3 ======================================== classification 1 ====================Point{x=3.5, y=12.5}centroid is Point{x=3.5, y=12.5}==================== classification 2 ====================Point{x=6.8, y=12.6}Point{x=7.8, y=12.2}Point{x=8.2, y=11.1}Point{x=9.6, y=11.1}centroid is Point{x=8.1, y=11.75}==================== classification 3 ====================Point{x=4.4, y=6.5}Point{x=4.8, y=1.1}Point{x=5.3, y=6.4}Point{x=6.6, y=7.7}Point{x=8.2, y=4.5}Point{x=8.4, y=6.9}Point{x=9.0, y=3.4}centroid is Point{x=6.671428, y=5.2142863}==================== classification 4 ====================Point{x=6.0, y=19.9}Point{x=6.2, y=18.5}Point{x=5.3, y=19.4}Point{x=7.6, y=17.4}centroid is Point{x=6.275, y=18.800001}==================== classification 5 ====================Point{x=0.8, y=9.8}Point{x=1.2, y=11.6}Point{x=2.8, y=9.6}Point{x=3.8, y=9.9}centroid is Point{x=2.15, y=10.225}==================== classification 6 ====================Point{x=6.1, y=14.3}centroid is Point{x=6.1, y=14.3}
代码下载地址:
/download/huangyueranbbc/10267041
github:
/huangyueranbbc/KmeansDemo
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