凝聚层次聚类算法 matlab源码.docx

共享一个在数据挖掘课程中作为示例使用的凝聚层次聚类算法源码，供大家学习交流使用时将源码复制进一个新的function并更改m文件的文件名为agnes即可-2016年4月10日以下为程序的matlab源码：function cluster=agnes(pointset,targetclusternum,method)% 凝聚层次聚类算法% 输入：点集pointset、目标簇数targetclusternum、簇间差异度度量方式method% 点集pointset为n*m矩阵，包含n个点，每个点有m个属性% 目标簇数targetclusternum为一个整数，0targetclusternumn% method为字符串，对应不同距离度量方式：% method=min:最小距离度量;method=max:最大距离度量;method=mean:均值距离度量;method=avg:平均距离度量;% 输出：cluster为长度为n的向量，表示各点所对应簇的类别标记% 调用方式示例：cluster=agnes(pointset,3,max);% 表示将pointset使用最大距离度量方式聚为3个类，将通过cluster变量返回类标记 pointnum=size(pointset,1);cluster=1:pointnum; %每个点对应簇标记 i=0;while true i=i+1; unicluster=unique(cluster);clusternum=size(unicluster,2); %当前簇数目。初始默认每个点为单独的一个簇 if clusternum=targetclusternum%若聚类数满足结束条件则算法结束 break; end %计算簇间差异度 clusterdist=zeros(clusternum); if strcmp(method,mean)=1%计算各簇间差异度：平均距离 clustermean=; for c=1:clusternum%计算各簇均值 unicluster=unique(cluster); subcluster=pointset(cluster=unicluster(c),:); clustermean=clustermean;unicluster(c),mean(subcluster,1); end for d=1:size(clustermean,1) %计算各簇间距离 clusterdist(:,d)=sqrt(sum(clustermean(:,2:end)-repmat(clustermean(d,2:end),size(clustermean,1),1).2,2); end clusterdist=clusterdist+eye(size(clusterdist,1)*max(max(clusterdist)*100; %将对角线设为最大值 elseif strcmp(method,min)=1%计算各簇间差异度：最小距离 for c1=1:clusternum for c2=c1+1:clusternum subpointset1=pointset(cluster=unicluster(c1),:); subpointset2=pointset(cluster=unicluster(c2),:); subpointset1expend=; for c3=1:size(subpointset1,1) subpointset1expend=subpointset1expend;repmat(subpointset1(c3,:),size(subpointset2,1),1); end subpointset2expend=repmat(subpointset2,size(subpointset1,1),1); dist=sqrt(sum(subpointset1expend-subpointset2expend).2,2); clusterdist(c1,c2)=min(dist); %取最小值 end end clusterdist=clusterdist+clusterdist; %将三角阵转换为对称阵 clusterdist=clusterdist+eye(size(clusterdist,1)*max(max(clusterdist)*100; %将对角线设为最大值 elseif strcmp(method,max)=1%计算各簇间差异度：最大距离 for c1=1:clusternum for c2=c1+1:clusternum subpointset1=pointset(cluster=unicluster(c1),:); subpointset2=pointset(cluster=unicluster(c2),:); subpointset1expend=; for c3=1:size(subpointset1,1) subpointset1expend=subpointset1expend;repmat(subpointset1(c3,:),size(subpointset2,1),1); end subpointset2expend=repmat(subpointset2,size(subpointset1,1),1); dist=sqrt(sum(subpointset1expend-subpointset2expend).2,2); clusterdist(c1,c2)=max(dist); %取最大值 end end clusterdist=clusterdist+clusterdist; clusterdist=clusterdist+eye(size(clusterdist,1)*max(max(clusterdist)*100; elseif strcmp(method,avg)=1%计算各簇间差异度：均值距离 for c1=1:clusternum for c2=c1+1:clusternum subpointset1=pointset(cluster=unicluster(c1),:); subpointset2=pointset(cluster=unicluster(c2),:); subpointset1expend=; for c3=1:size(subpointset1,1) subpointset1expend=subpointset1expend;repmat(subpointset1(c3,:),size(subpointset2,1),1); end subpointset2expend=repmat(subpointset2,size(subpointset1,1),1); dist=sqrt(sum(subpointset1expend-subpointset2expend).2,2); clusterdist(c1,c2)=sum(dist)./size(dist,1); %取均值 end end clusterdist=clusterdist+clusterdist; clusterdist=clusterdist+eye(size(clusterdist,1)*max(max(clusterdist)*100; end %合并相异度最小的两个簇 rows,cols=find