K-means聚类
1.随机产生k个分类特征的中心点
2.计算数据点到中心点的距离
3.数据点到哪个中心点最近就分到哪个类
4.迭代:更新中心点位置,重新计算距离并分配类别,直到总体距离最小
load fisheriris figure; speciesNum=grp2idx(species); gscatter(meas(:,3),meas(:,4),speciesNum,['r','g','b']); xlabel('花瓣长度'); ylabel('花瓣宽度'); title('真实标记'); set(gca,'fontsize',12); set(gca,'fontweight','bold'); data=[meas(:,3),meas(:,4)]; K=3; %5此重复的全局最优解 [idx,cen]=kmeans(data,K,'Distance','sqeuclidean','Replicates',5,'Display','Final'); %调整标号 dist=sum(cen.^2,2); [dump,sortind]=sort(dist,'ascend'); newidx=zeros(size(idx)); for i=1:K newidx(idx==i)=find(sortind==i); end %花瓣长度和花瓣宽度散点图(kmeans分类) figure; gscatter(data(:,1),data(:,2),newidx,['r','g','b']); hold on scatter(cen(:,1),cen(:,2),300,'m'); hold off xlabel('花瓣长度'); ylabel('花瓣宽度'); title('kmeans分类'); set(gca,'fontsize',12); set(gca,'fontweight','bold');