【聚类】基于PCA+kmeans实现数据聚类附matlab代码.zip

clc; clear all; close all; filename = 'data.xlsx'; sheet = 2; X = xlsread(filename,sheet); %% PCA主成分分析 meanX = ones(size(X,1),1) * mean(X); %中心化处理 centredX = X - meanX; C = cov(centredX); %计算协方差矩阵 [W,Lambda] = eig(C); %W是特征向量组成的矩阵，Lambda是特征值组成的对角矩阵 ev = (diag(Lambda))'; %提取特征值 ev = ev(:,end:-1:1); %eig计算出的特征值是升序的，这里手动倒序（W同理） W = W(:,end:-1:1); Wr = W(:,1:10); %提取前N个主成分的特征向量 Tr = centredX * Wr; %新坐标空间的数据点 %% 开始聚类 cluster_num = 3; [index_cluster,cluster] = kmeans(Tr,cluster_num); %% 画出聚类效果 figure(1) a = unique(index_cluster); C = cell(1,length(a)); for i = 1:length(a) C(1,i) = {find(index_cluster==a(i))}; end for j = 1:cluster_num data_get = Tr(C{1,j},:); scatter(data_get(:,1),data_get(:,2),100,'filled','MarkerFaceAlpha',.6,'MarkerEdgeAlpha',.9); hold on end %绘制聚类中心 plot(cluster(:,1),cluster(:,2),'ks','LineWidth',2); hold on title_str = ['Kmeans聚类',' 聚类数为：',num2str(cluster_num)]; title(title_str)