K均值聚类算法的Java版实现代码示例

package org.algorithm;import java.util.ArrayList;import java.util.Random;public class Kmeans {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<float[]> dataSet;// 数据集链表 private ArrayList<float[]> center;// 中心链表 private ArrayList<ArrayList<float[]>> cluster;// 簇 private ArrayList<float> jc;// 误差平方和，k越接近dataSetLength，误差越小 private Random random;public void setDataSet(ArrayList<float[]> dataSet) {this.dataSet = dataSet;}public ArrayList<ArrayList<float[]>> getCluster() {return cluster;}public Kmeans(int k) {if (k <= 0) {k = 1;}this.k = k;}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<float>();}private void initDataSet() {dataSet = new ArrayList<float[]>();// 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]);}}private ArrayList<float[]> initCenters() {ArrayList<float[]> center = new ArrayList<float[]>();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;// 不清楚for循环导致j无法加1 // for(j=0;j<i;++j) // { // if(temp==randoms[j]); // { // break; // } // } while (j < i) {if (temp == randoms[j]) {break;}j++;}if (j == i) {flag = false;}}randoms[i] = temp;}// 测试随机数生成情况 // for(int i=0;i<k;i++) // { // System.out.println("test1:randoms["+i+"]="+randoms[i]); // } // System.out.println(); for (int i = 0; i < k; i++) {center.add(dataSet.get(randoms[i]));// 生成初始化中心链表}return center;}private ArrayList<ArrayList<float[]>> initCluster() {ArrayList<ArrayList<float[]>> cluster = new ArrayList<ArrayList<float[]>>();for (int i = 0; i < k; i++) {cluster.add(new ArrayList<float[]>());}return cluster;}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;}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;}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));// System.out.println("test2:"+"dataSet["+i+"],center["+j+"],distance="+distance[j]);}int minLocation = minDistance(distance);// System.out.println("test3:"+"dataSet["+i+"],minLocation="+minLocation); // System.out.println(); 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}}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;}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);}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);}}}public void printDataArray(ArrayList<float[]> 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("===================================");}private void kmeans() {init();// printDataArray(dataSet,"initDataSet"); // printDataArray(center,"initCenter"); // 循环分组，直到误差不变为止 while (true) {clusterSet();// for(int i=0;i<cluster.size();i++) // { // printDataArray(cluster.get(i),"cluster["+i+"]"); // } countRule();// System.out.println("count:"+"jc["+m+"]="+jc.get(m)); // System.out.println(); // 误差不变了，分组完成 if (m != 0) {if (jc.get(m) - jc.get(m - 1) == 0) {break;}}setNewCenter();// printDataArray(center,"newCenter"); m++;cluster.clear();cluster = initCluster();}// System.out.println("note:the times of repeat:m="+m);//输出迭代次数}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();}}