1.Kmeans聚类算法简介
kmeans聚类算法是一种迭代求解的聚类分析算法。其实现步骤如下:
(1) 随机选取K个对象作为初始的聚类中心
(2) 计算每个对象与各个种子聚类中心之间的距离,把每个对象分配给距离它最近的聚类中心。
(3) 聚类中心以及分配给它们的对象就代表一个聚类。每分配一个样本,聚类的聚类中心会根据聚类中现有的对象被重新计算。
(4) 重复步骤(2)、(3),直到满足某个终止条件。终止条件可以是聚类中心再发生变化或者误差平方和局部最小等。
2.Kmeans聚类算法的代码实现
(1) 首先,加载需要进行分类的数据集。
data(:,1)=[90,35,52,83,64,24,49,92,99,45,19,38,1,71,56,97,63,...
32,3,34,33,55,75,84,53,15,88,66,41,51,39,78,67,65,25,40,77,...
13,69,29,14,54,87,47,44,58,8,68,81,31];
data(:,2)=[33,71,62,34,49,48,46,69,56,59,28,14,55,41,39,...
78,23,99,68,30,87,85,43,88,2,47,50,77,22,76,94,11,80,...
51,6,7,72,36,90,96,44,61,70,60,75,74,63,40,81,4];
figure(1)
scatter(data(:,1),data(:,2),'LineWidth',2)
title("原始数据散点图")
原始数据绘制散点图如下所示:
(2) 设置分类数量并调用自己编写的kmeans聚类函数
cluster_num=4;
[index_cluster,cluster] = kmeans_func(data,cluster_num);
function [index_cluster,cluster] = kmeans_func(data,cluster_num)
%% 原理推导Kmeans聚类算法
[m,n]=size(data);
cluster=data(randperm(m,cluster_num),:);%从m个点中随机选择cluster_num个点作为初始聚类中心点
epoch_max=1000;%最大次数
therad_lim=0.001;%中心变化阈值
epoch_num=0;
while(epoch_num epoch_num=epoch_num+1; % distance1存储每个点到各聚类中心的欧氏距离 for i=1:cluster_num distance=(data-repmat(cluster(i,:),m,1)).^2; distance1(:,i)=sqrt(sum(distance')); end [~,index_cluster]=min(distance1');%index_cluster取值范围1~cluster_num % cluster_new存储新的聚类中心 for j=1:cluster_num cluster_new(j,:)=mean(data(find(index_cluster==j),:)); end %如果新的聚类中心和上一轮的聚类中心距离和大于therad_lim,更新聚类中心,否则算法结束 if (sqrt(sum((cluster_new-cluster).^2))>therad_lim) cluster=cluster_new; else break; end end end (3) 对分类结果和最终的聚类中心进行可视化展示 %% 画出聚类效果 figure(2) % subplot(2,1,1) a=unique(index_cluster); %找出分类出的个数 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=data(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 sc_t=mean(silhouette(data,index_cluster')); title_str=['原理推导K均值聚类',' 聚类数为:',num2str(cluster_num),' SC轮廓系数:',num2str(sc_t)]; title(title_str) 3.完整实现代码 clc;clear;close all; data(:,1)=[90,35,52,83,64,24,49,92,99,45,19,38,1,71,56,97,63,... 32,3,34,33,55,75,84,53,15,88,66,41,51,39,78,67,65,25,40,77,... 13,69,29,14,54,87,47,44,58,8,68,81,31]; data(:,2)=[33,71,62,34,49,48,46,69,56,59,28,14,55,41,39,... 78,23,99,68,30,87,85,43,88,2,47,50,77,22,76,94,11,80,... 51,6,7,72,36,90,96,44,61,70,60,75,74,63,40,81,4]; figure(1) scatter(data(:,1),data(:,2),'LineWidth',2) title("原始数据散点图") cluster_num=4; [index_cluster,cluster] = kmeans_func(data,cluster_num); %% 画出聚类效果 figure(2) % subplot(2,1,1) a=unique(index_cluster); %找出分类出的个数 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=data(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 sc_t=mean(silhouette(data,index_cluster')); title_str=['原理推导K均值聚类',' 聚类数为:',num2str(cluster_num),' SC轮廓系数:',num2str(sc_t)]; title(title_str) 4. 总结 以上就是matlab实现kmeans聚类算法的全部代码,可在上述代码的基础上进行数据集替换,应用于其它场景。如果有不懂的小伙伴儿,欢迎评论留言或者私信,代码订制也可私信博主。 好文链接
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