K-means算法Java实现
public class KMeansCluster {
private int k;//簇的个数 private int num = 100000;//迭代次数 private List<double> datas;//原始样本集 private String address;//样本集路径 private List<point> data = new ArrayList<point>(); private AbstractDistance distance = new AbstractDistance() { @Override public double getDis(Point p1, Point p2) { //欧几里德距离 return Math.sqrt(Math.pow(p1.getX() - p2.getX(), 2) + Math.pow(p1.getY() - p2.getY(), 2)); } }; public KMeansCluster(int k, int num, String address) { this.k = k; this.num = num; this.address = address; } public KMeansCluster(int k, String address) { this.k = k; this.address = address; } public KMeansCluster(int k, List<double> datas) { this.k = k; this.datas = datas; } public KMeansCluster(int k, int num, List<double> datas) { this.k = k; this.num = num; this.datas = datas; } private void check() { if (k == 0) throw new IllegalArgumentException("k must be the number > 0"); if (address == null && datas == null) throw new IllegalArgumentException("program can't get real data"); } /** * 初始化数据 * * @throws java.io.FileNotFoundException */ public void init() throws FileNotFoundException { check(); //读取文件，init data //处理原始数据 for (int i = 0, j = datas.size(); i < j; i++) data.add(new Point(i, datas.get(i), 0)); } /** * 第一次随机选取中心点 * * @return */ public Set<point> chooseCenter() { Set<point> center = new HashSet<point>(); Random ran = new Random(); int roll = 0; while (center.size() < k) { roll = ran.nextInt(data.size()); center.add(data.get(roll)); } return center; } /** * @param center * @return */ public List<cluster> prepare(Set<point> center) { List<cluster> cluster = new ArrayList<cluster>(); Iterator<point> it = center.iterator(); int id = 0; while (it.hasNext()) { Point p = it.next(); if (p.isBeyond()) { Cluster c = new Cluster(id++, p); c.addPoint(p); cluster.add(c); } else cluster.add(new Cluster(id++, p)); } return cluster; } /** * 第一次运算，中心点为样本值 * * @param center * @param cluster * @return */ public List<cluster> clustering(Set<point> center, List<cluster> cluster) { Point[] p = center.toArray(new Point[0]); TreeSet<distence> distence = new TreeSet<distence>();//存放距离信息 Point source; Point dest; boolean flag = false; for (int i = 0, n = data.size(); i < n; i++) { distence.clear(); for (int j = 0; j < center.size(); j++) { if (center.contains(data.get(i))) break; flag = true; // 计算距离 source = data.get(i); dest = p[j]; distence.add(new Distence(source, dest, distance)); } if (flag == true) { Distence min = distence.first(); for (int m = 0, k = cluster.size(); m < k; m++) { if (cluster.get(m).getCenter().equals(min.getDest())) cluster.get(m).addPoint(min.getSource()); } } flag = false; } return cluster; } /** * 迭代运算，中心点为簇内样本均值 * * @param cluster * @return */ public List<cluster> cluster(List<cluster> cluster) { // double error; Set<point> lastCenter = new HashSet<point>(); for (int m = 0; m < num; m++) { // error = 0; Set<point> center = new HashSet<point>(); // 重新计算聚类中心 for (int j = 0; j < k; j++) { List<point> ps = cluster.get(j).getMembers(); int size = ps.size(); if (size < 3) { center.add(cluster.get(j).getCenter()); continue; } // 计算距离 double x = 0.0, y = 0.0; for (int k1 = 0; k1 < size; k1++) { x += ps.get(k1).getX(); y += ps.get(k1).getY(); } //得到新的中心点 Point nc = new Point(-1, x / size, y / size, false); center.add(nc); } if (lastCenter.containsAll(center))//中心点不在变化，退出迭代 break; lastCenter = center; // 迭代运算 cluster = clustering(center, prepare(center)); // for (int nz = 0; nz < k; nz++) { // error += cluster.get(nz).getError();//计算误差 // } } return cluster; } /** * 输出聚类信息到控制台 * * @param cs */ public void out2console(List<cluster> cs) { for (int i = 0; i < cs.size(); i++) { System.out.println("No." + (i + 1) + " cluster:"); Cluster c = cs.get(i); List<point> p = c.getMembers(); for (int j = 0; j < p.size(); j++) { System.out.println("\t" + p.get(j).getX() + " "); } System.out.println(); } } }
