matlab--密度峰值聚类--算法.rar

clc clear all close all %% 加载数据 load('../测试数据/D31.mat') data=A(:,1:2); %% 设置相关参数 cut_dist=0.8; %% 统计每个数据点密度 disp("统计数据密度。。。") data_density=cal_density(data,cut_dist); %% 计算每个数据点的delta disp("计算数据delta。。。") data_delta=cal_delta(data,data_density); %% 寻找聚类中心点 disp("寻找中心点。。。") [center,center_index]=find_center(data,data_delta,data_density,cut_dist); figure; Plot_find_center(data,center) %% 开始聚类 disp("聚类。。。") cluster=Clustering(data,center,center_index,data_density); figure; PlotClusterinResult(data,cluster) %% 密度计算函数 function data_density=cal_density(data,cut_dist) data_len=size(data,1); data_density=zeros(1,data_len); for idata_len=1:data_len temp_dist=pdist2(data,data(idata_len,:)); data_density(idata_len)=sum(temp_dist<=cut_dist); end end %% 计算delta function data_delta=cal_delta(data,data_density) data_len=size(data,1); data_delta=zeros(1,data_len); for idata_len=1:data_len index=data_density>data_density(idata_len); if sum(index)~=0 data_delta(idata_len)=min(pdist2(data(idata_len,:),data(index,:))); else data_delta(idata_len)=max(pdist2(data(idata_len,:),data)); end end end %% 寻找聚类中心点 function [center,center_index]=find_center(data,data_delta,data_density,cut_dist) R=data_density.*data_delta; [sort_R,R_index]=sort(R,"descend"); gama=abs(sort_R(1:end-1)-sort_R(2:end)); [sort_gama,gama_idnex]=sort(gama,"descend"); gmeans=mean(sort_gama(2:end)); %寻找疑似聚类中心点 temp_center=data(R_index(gama>gmeans),:); temp_center_index=R_index(gama>gmeans); %进一步筛选中心点 temp_center_dist=pdist2(temp_center,temp_center); temp_center_len=size(temp_center,1); center=[]; center_index=[]; %判断中心点之间距离是否小于2倍截断距离并中心点去重 for icenter_len=1:temp_center_len temp_index=find(temp_center_dist(icenter_len,:)<2*cut_dist); [~,max_density_index]=max(data_density(temp_center_index(temp_index))); if sum(center_index==temp_center_index(temp_index(max_density_index)))==0 center=[center;temp_center(temp_index(max_density_index),:)]; center_index=[center_index,temp_center_index(temp_index(max_density_index))]; end % center(icenter_len,:)=temp_center(temp_index(max_density_index),:); end end %% 聚类算法 function cluster=Clustering(data,center,center_index,data_density) data_len=size(data,1); data_dist=pdist2(data,data); cluster=zeros(1,data_len); % 标记中心点序号 for i=1:size(center_index,2) cluster(center_index(i))=i; end % 对数据密度进行降序排序 [sort_density,sort_index]=sort(data_density,"descend"); for idata_len=1:data_len %判断当前数据点是否被分类 if cluster(sort_index(idata_len))==0 near=sort_index(idata_len); while 1 near_density=find(data_density>data_density(near)); near_dist=data_dist(near,near_density); [~,min_index]=min(near_dist); if cluster(near_density(min_index)) cluster(sort_index(idata_len))=cluster(near_density(min_index)); break; else near=near_density(min_index); end end end end end