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程序流程图:

Hierarchical(MIN)核心功能函数,采用vector<vector >::dTable存储两点之间的距离。计算每两个point间的距离并保存到distance table中;判断是否达到需要聚类的cluster数量,若是,将point信息写入clustering文件,程序结束。否则,合并两个具有最小距离的point,将两个point中的所有node全部加入到一个point中,删除拷贝node数组后的多余point,跟新dataset数组。然后更新distance table,删除被合并point对应的distance table中行与列,进入下一步循环。

/*
	K-means Algorithm
	15S103182
	Ethan
*/
#include <iostream>
#include <sstream>
#include <fstream>
#include <vector>
#include <iterator>
#include <cmath>
#include <stack> 
#include <limits>
using namespace std;

class node{
public:
	float x;
	float y;
	node(float a,float b){
		x = a;
		y = b;
	}
};

class point{
public:
	float x;
	float y;
	vector<node> nds;
	point (float a,float b){
		x = a;
		y = b;
		node t(a,b);
		nds.push_back(t);
	}
};
float stringToFloat(string i){
	stringstream sf;
	float score=0;
	sf<<i;
	sf>>score;
	return score;
}
vector<point> openFile(const char* dataset){
	fstream file;
	file.open(dataset,ios::in);
	if(!file) 
    {
        cout <<"Open File Failed!" <<endl;
        vector<point> a;
        return a;
    } 
	vector<point> data;
	while(!file.eof()){
		string temp;
		file>>temp;
		int split = temp.find(",",0);
		point p(stringToFloat(temp.substr(0,split)),stringToFloat(temp.substr(split+1,temp.length()-1)));
		data.push_back(p);
	}
	file.close();
	cout<<"Read data successfully!"<<endl;
	return data;
}
float squareDistance(point a,point b){
	return sqrt((a.x-b.x)*(a.x-b.x)+(a.y-b.y)*(a.y-b.y));
}
void HierarchicalClustering(vector<point> dataset,int clusters){
	//Similarity table:distance table 
	vector<vector<float> > dTable;
	for(int i=0;i<dataset.size();i++){
		vector<float> temp;
		for(int j=0;j<dataset.size();j++){
			if(j>i)
				temp.push_back(squareDistance(dataset[i],dataset[j]));
			else if(j<i)
				temp.push_back(dTable[j][i]);
			else
				temp.push_back(0);		    
		}
		dTable.push_back(temp);
	}
	int len = dataset.size();
	cout<<"len:"<<len<<endl;
	while(dataset.size()>clusters){
		//Merge two closest clusters
		float minDt =INT_MAX;
		int mi,mj;
		for(int i=0;i<dTable.size();i++){
			for(int j=i+1;j<dTable[i].size();j++){
				 if(dTable[i][j]<minDt){
				 	minDt = dTable[i][j];
				 	mi = i;
				 	mj = j;
				 }
			}
		}
		for(int i=0;i<dataset[mj].nds.size();i++){
			dataset[mi].nds.push_back(dataset[mj].nds[i]);
		}
		//rm
		vector<point>::iterator imj = dataset.begin();
		imj += mj;
		dataset.erase(imj);
		
		//Update the dTable
		for(int j=0;j<dTable.size();j++){
			if(j==mi||j==mj) continue;
			if(dTable[mi][j]>dTable[mj][j]){
				dTable[mi][j] = dTable[mj][j];
				dTable[j][mi] = dTable[mi][j];
			}
		}
		//rm
		vector<vector<float> >::iterator ii = dTable.begin();
		ii += mj;
		dTable.erase(ii);
		for(int i=0;i<dTable.size();i++){
			vector<float>::iterator ij = dTable[i].begin();
			ij += mj;
			dTable[i].erase(ij);
		}
	}
	fstream clustering;
	clustering.open("clustering.txt",ios::out);
	for(int i=0;i<dataset.size();i++){
		for(int j=0;j<dataset[i].nds.size();j++)
			clustering<<dataset[i].nds[j].x<<","<<dataset[i].nds[j].y<<","<<i+1<<"
";
	}
	cout<<dataset.size();
	clustering.close();
}

int main(int argc, char** argv) {
	vector<point> dataset = openFile("dataset3.txt");
	HierarchicalClustering(dataset,15);
	return 0;
}

数据文件格式:(x,y)

运行结果格式:(x,y,cluster)

具体文件格式见DBSCAN篇:http://blog.csdn.net/k76853/article/details/50440182

图形化展现:

总结:

Hierarchical(MIN)算法能够很好处理非圆形状的数据。但对于噪音和离群点,Hierarchical(MIN)算法具有局限性。由于层次聚类具有不可恢复性,一旦聚类,就难以撤销聚类操作,刚开始编码的时候走了不少弯路,后来果断决定用数组存储node信息,方便cluster合并。对于近邻表的更新,也遇到一点小麻烦,由于二级指针对于行列的删除比较复杂,果断采用动态数组vector来存储距离信息,二级vector对于行的删除非常直接简单,对于列的删除也只需O(n)操作,比较简洁高效。