Kmeans-java.zip_JAVA 聚类_k均值聚类 java_聚类KMEANS算法_高斯聚类 JAVA

import java.util.ArrayList; import java.util.Random; public class Kmeans { private int k; //参数k private int m; //迭代次数 private int dataSetLength; //数据集元素个数，即数据集长度 private ArrayList<float[]> dataSet; //数据集链表 private ArrayList<float[]> center; //中心链表 private ArrayList<ArrayList<float[]>> cluster; private ArrayList<Float> Jc; //误差平方和 private Random random; /* * 构造方法 */ public Kmeans(int k){ this.k = k; } /* * 初始化方法 */ public void init(){ m = 0; random = new Random(); dataSet = initDataSet(); //初始化数据集 dataSetLength = dataSet.size(); center = initCenters(); //初始化中心 cluster = initCluster(); //初始化簇集，分配内存，但元素为空 Jc = new ArrayList<Float>(); //初始化误差平方和 } /* * 初始化数据集方法 */ public ArrayList<float[]> initDataSet(){ ArrayList<float[]> dataSet = new ArrayList<float[]>(); 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]); } return dataSet; } /* * 初始化中心方法 */ public 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; for(j = 0; j < i; j ++){ if(temp == randoms[j]){ break; } } if(j == i){ flag = false; } } randoms[i] = temp; } /* //测试随机数组生成情况 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; } /* * 初始化簇集方法 */ public 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; } /* * 求距离方法 */ public 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; } /* * 求最小距离位置方法 */ public 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){ //如果和当前最短距离相等，则随机选取一个 if(random.nextInt(10) < 5){ minLocation = i; } } } return minLocation; } /* * 生成簇集元素方法 */ public 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 + "], centers[" + j + "], distance = " + distance[j]); //测试元素与中心距离 } int minLocation = minDistance(distance); //System.out.println("test2: " + "dataSet[" + i + "], minLocation = " + minLocation); //测试最小距离位置 //System.out.println(); //测试用 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 } } /* * 求误差平方的方法 */ public float errorSquare(float[] element, float[] center){ float x = element[0] - center[0]; float y = element[1] - center[1]; float errorSquare = x*x + y*y; return errorSquare; } /* * 计算误差平方和准则函数方法 */ public 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); } /* * 计算新的簇中心方法 */ public void findNewCenter(){ 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]; } newCenter[0] = newCenter[0] / n; newCenter[1] = newCenter[1] / n; center.set(i, newCenter); } } } /* * 打印数据数组 */ 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(); } /* * Kmeans算法核心过程方法 */ public void kmeans(){ init(); //初始化 printDataArray(dataSet, "initDataSet"); //输出初始化数据集 printDataArray(center, "initCenter"); //输出初始化中心 while(true){ clusterSet(); //生成簇集元素 for (int i = 0; i < cluster.size(); i++) { printDataArray(cluster.get(i), "cluster[" + i + "]"); //输出簇集生成结果 } countRule(); //计算误差平方和 System.out.println("count:" + "Jc[" + m + "] = " + Jc.get(m)); //输出误差平方和 System.out.println(); //判断退出迭代条件 if(m != 0){ if (Jc.get(m) - Jc.get(m - 1) == 0) { break; } } findNewCenter(); //计算新的中心 printDataArray(center, "newCenter"); //输出新的中心 m++; cluster.clear(); //簇集清空 cluster = initCluster(); //簇集初始化 } System.out.println("note: the times of repeat: m = " + m); //输出迭代次数 } /* * 主函数 */ public static void main(String[] args){ long startTime = System.currentTimeMillis(); //获取开始时间 System.out.println("note: program begins."); Kmeans myKmeans = new Kmeans(3); myKmeans.kmeans(); //调用Kmeans核心方法 long endTime = System.currentTimeMillis(); //获取结束时间 System.out.println("note: running time = " +(endTime - startTime) + "ms."); System.out.println("note: program ends."); } }