ISODATA算法是在k-均值算法的基础上,增加对聚类结果的“合并”和“分裂”两个操作,并设定算法运行控制参数的一种聚类算法。迭代次数会影响最终结果,迭代参数选择很重要。
// isodata-cluster.cpp : 定义控制台应用程序的入口点。 // #include "stdafx.h" #include<vector> #include<algorithm> #include<set> #include<time.h> #include<cstdlib> #include<iostream> #include <iterator> using namespace std; class isodata { private: unsigned int K;// 所想要分成的类别数 unsigned int thetaN;//一个类别至少应具有的样本数目,如小于此数就不作为一个独立的聚类 double theta_c;// 聚类中心之间距离的最小值,即归并系数,如小于此数,两个聚类进行合并 double theta_s;// 一个类别中样本标准差最大值 unsigned int maxcombine;// 每次迭代最多可归并对数 unsigned int maxiteration;// 最大迭代次数 unsigned int dim; double meandis; double alpha; unsigned int current_iter; vector<vector<int>>dataset; typedef vector<double> Centroid; struct Cluster { vector<int>clusterID; Centroid center; double inner_meandis; vector<double>sigma; }; vector<Cluster>clus; private: void init(); void assign(); void check_thetaN(); void update_centers(); void update_center(Cluster &aa); void update_sigma(Cluster &aa); void calmeandis(); void choose_nextstep(); double distance(const Centroid ¢er, const int k); double distance(const Centroid &cen1, const Centroid &cen2); void split(const int kk); void check_for_split(); void merge(const int k1, const int k2); void check_for_merge(); void prepare_for_next_itration(); void show_result(); public: isodata() { time_t t; srand(time(&t)); } void generate_data(); void apply(); void set_paras(); }; void isodata::show_result() { int num = 0; for (int i = 0; i < clus.size(); i++) { char string[100]; sprintf(string, "第个%d簇:", i); cout << string << endl; cout << "中心为 (" << clus[i].center[0] << "," << clus[i].center[1] << ")" << endl; for (int j = 0; j < clus[i].clusterID.size(); j++) { sprintf(string, "编号%d ", clus[i].clusterID[j]); cout << string << "(" << dataset[clus[i].clusterID[j]][0] << "," << dataset[clus[i].clusterID[j]][1] << ")" << endl; num++; } cout << endl << endl; } _ASSERTE(num == dataset.size()); } void isodata::generate_data() { int datanums = 100; dim = 2; for (int i = 0; i < datanums; i++) { vector<int>data; data.resize(dim); for (int j = 0; j < dim; j++) data[j] = double(rand()) / RAND_MAX * 100; dataset.push_back(data); } } void isodata::set_paras() { K = 5; theta_c = 5; theta_s = 0.01; maxiteration = 10; maxcombine = 2; thetaN = 5; alpha = 0.3; } void isodata::prepare_for_next_itration() { for (int i = 0; i < clus.size(); i++) clus[i].clusterID.clear(); } void isodata::apply() { init(); while (current_iter < maxiteration) { current_iter++; assign(); check_thetaN(); update_centers(); calmeandis(); choose_nextstep(); if (current_iter < maxiteration) prepare_for_next_itration(); } show_result(); } double isodata::distance(const Centroid &cen, const int k) { double dis = 0; for (int i = 0; i < dim; i++) dis += pow(cen[i] - dataset[k][i], 2); return sqrt(dis); } double isodata::distance(const Centroid ¢er1, const Centroid& center2) { double dis = 0; for (int i = 0; i < dim; i++) dis += pow(center1[i] - center2[i], 2); return sqrt(dis); } /*第一步:输入N个模式样本{xi, i = 1, 2, …, N} 预选Nc个初始聚类中心*/ void isodata::init() { clus.resize(K); set<int>aa; for (int i = 0; i < K; i++) { clus[i].center.resize(dim); int id = double(rand()) / RAND_MAX*dataset.size(); while (aa.find(id) != aa.end()) { id = double(rand()) / RAND_MAX*dataset.size(); } aa.insert(id); for (int j = 0; j < dim; j++) clus[i].center[j] = dataset[id][j]; } } /*第二步:将N个模式样本分给最近的聚类Sj */ void isodata::assign() { for (int i = 0; i < dataset.size(); i++) { double mindis = 100000000; int th = -1; for (int j = 0; j < clus.size(); j++) { double dis = distance(clus[j].center, i); if (dis < mindis) { mindis = dis; th = j; } } clus[th].clusterID.push_back(i); } } /*第三步:如果Sj中的样本数目Sj<θN, 则取消该样本子集,此时Nc减去1*/ void isodata::check_thetaN() { vector<int>toerase; for (int i = 0; i < clus.size(); i++) { if (clus[i].clusterID.size() < thetaN) { toerase.push_back(i); for (int j = 0; j < clus[i].clusterID.size(); j++) { double mindis = 10000000; int th = -1; for (int m = 0; m < clus.size(); m++) { if (m == i) continue; double dis = distance(clus[m].center, clus[i].clusterID[j]); if (dis < mindis) { mindis = dis; th = m; } } clus[th].clusterID.push_back( clus[i].clusterID[j]); } clus[i].clusterID.clear(); } } for (vector<Cluster>::iterator it = clus.begin(); it != clus.end();) { if (it->clusterID.empty()) it = clus.erase(it); else it++; } } void isodata::update_center(Cluster &aa) { Centroid temp; temp.resize(dim); for (int j = 0; j < aa.clusterID.size(); j++) { for (int m = 0; m < dim; m++) temp[m] += dataset[aa. clusterID[j]][m]; } for (int m = 0; m < dim; m++) temp[m] /= aa.clusterID.size(); aa.center = temp; } /*第四步:修正各聚类中心*/ void isodata::update_centers() { for (int i = 0; i < clus.size(); i++) { update_center(clus[i]); } } void isodata::update_sigma(Cluster&bb) { bb.sigma.clear(); bb.sigma.resize(dim); for (int j = 0; j < bb.clusterID.size(); j++) for (int m = 0; m < dim; m++) bb.sigma[m] += pow(bb.center[m] - dataset[bb.clusterID[j]][m], 2); for (int m = 0; m < dim; m++) bb.sigma[m] = sqrt(bb.sigma[m] / bb.clusterID.size()); } /*五六步合并*/ /*第五步:计算各聚类域Sj中模式样本与各聚类中心间的平均距离*/ /*第六步:计算全部模式样本和其对应聚类中心的总平均距离*/ void isodata::calmeandis() { meandis = 0; for (int i = 0; i < clus.size(); i++) { double dis = 0; for (int j = 0; j < clus[i]. clusterID.size(); j++) { dis += distance(clus[i].center, clus[i].clusterID[j]); } meandis += dis; clus[i].inner_meandis = dis / clus[i].clusterID.size(); } meandis /= dataset.size(); } /*第七步:判别下一步进行分裂或合并或迭代运算*/ void isodata::choose_nextstep() { if (current_iter == maxiteration) { theta_c = 0; //goto step 11 check_for_merge(); } else if (clus.size() < K / 2) { check_for_split(); } else if (current_iter % 2 == 0 || clus.size() >= 2 * K) { //goto step 11 check_for_merge(); } else { check_for_split(); } } /*八、九、十步合并为分裂操作*/ /*第八步:计算每个聚类中样本距离的标准差向量*/ /*第九步:求每一标准差向量{σj, j = 1, 2, …, Nc}中的最大分量*/ /*第十步:分裂*/ void isodata::check_for_split() { for (int i = 0; i < clus.size(); i++) { update_sigma(clus[i]); } while (true) { bool flag = false; for (int i = 0; i < clus.size(); i++) { for (int j = 0; j < dim; j++) { if (clus[i].sigma[j] > theta_s && (clus[i].inner_meandis>meandis&& clus[i].clusterID.size()> 2 * (thetaN + 1) || clus.size() < K / 2)) { flag = true; split(i); } } } if (!flag) break; else calmeandis(); } } void isodata::split(const int kk) { Cluster newcluster; newcluster.center.resize(dim); int th = -1; double maxval = 0; for (int i = 0; i < dim; i++) { if (clus[kk].sigma[i] > maxval) { maxval = clus[kk].sigma[i]; th = i; } } for (int i = 0; i < dim; i++) { newcluster.center[i] = clus[kk].center[i]; } newcluster.center[th] -= alpha*clus[kk].sigma[th]; clus[kk].center[th] += alpha*clus[kk].sigma[th]; for (int i = 0; i < clus[kk].clusterID.size(); i++) { double d1 = distance(clus[kk].center, clus[kk].clusterID[i]); double d2 = distance(newcluster.center, clus[kk].clusterID[i]); if (d2 < d1) newcluster.clusterID.push_back(clus[kk].clusterID[i]); } vector<int>cc; cc.reserve(clus[kk].clusterID.size()); vector<int>aa; //insert_iterator<set<int, less<int> > >res_ins(aa, aa.begin()); set_difference(clus[kk].clusterID.begin(), clus[kk].clusterID.end(), newcluster.clusterID.begin(), newcluster.clusterID.end(), inserter(aa, aa.begin()));//差集 clus[kk].clusterID = aa; //应该更新meandis sigma。。。 update_center(newcluster); update_sigma(newcluster); update_center(clus[kk]); update_sigma(clus[kk]); clus.push_back(newcluster); } /*第十一步:计算全部聚类中心的距离*/ /*第十二步:比较Dij 与θc 的值,将Dij <θc 的值按最小距离次序递增排列*/ /*第十三步:将距离为 的两个聚类中心 和 合并*/ void isodata::check_for_merge() { vector<pair<pair<int, int>, double>>aa; for (int i = 0; i < clus.size(); i++) { for (int j = i + 1; j < clus.size(); j++) { double dis = distance(clus[i].center, clus[j].center); if (dis < theta_c) { pair<int, int>bb(i, j); aa.push_back(pair<pair<int, int>, double>(bb, dis)); } } } // 利用函数对象实现升降排序 struct CompNameEx { CompNameEx(bool asce) : asce_(asce) {} bool operator()(pair<pair<int, int>, double>const& pl, pair<pair<int, int>, double>const& pr) { return asce_ ? pl.second < pr.second : pr.second < pl.second; // 《Eff STL》条款21: 永远让比较函数对相等的值返回false } private: bool asce_; }; sort(aa.begin(), aa.end(), CompNameEx(true)); set<int>bb; int combinenus = 0; for (int i = 0; i < aa.size(); i++) { if (bb.find(aa[i].first.first) == bb.end() && bb.find(aa[i].first.second) == bb.end()) { bb.insert(aa[i].first.first); bb.insert(aa[i].first.second); merge(aa[i].first.first, aa[i].first.second); combinenus++; if (combinenus >= maxcombine) break; } } for (vector<Cluster>::iterator it = clus.begin(); it != clus.end();) { if (it->clusterID.empty()) { it = clus.erase(it); } else it++; } } void isodata::merge(const int k1, const int k2)//k1、k2顺序不能变 { for (int i = 0; i < dim; i++) clus[k1].center[i] = (clus[k1].center[i] * clus[k1].clusterID.size() + clus[k2].center[i] * clus[k2].clusterID.size()) / double(clus[k1].clusterID.size() + clus[k2].clusterID.size()); //clus[k1].clusterID.insert(clus[k1].clusterID.end(), // clus[k2].clusterID.begin(), clus[k2].clusterID.end()); clus[k2].clusterID.clear(); } int _tmain(int argc, _TCHAR* argv[]) { /*vector<int>aa; aa.push_back(1); aa.push_back(2); aa.push_back(3); aa.push_back(4); aa.push_back(5); for (vector<int>::iterator it = aa.begin(); it != aa.end(); ) { cout << *it << endl; //it = aa.erase(it); //if (it == aa.end()) // break; if (*it > 3) { it = aa.insert(it+1, 2); cout << *it << endl; } else it++; }*/ isodata iso; iso.generate_data(); iso.set_paras(); iso.apply(); system("pause"); return 0; }