【matlab代码】k-means 聚类的示例代码.zip

function kmeansSample() close all; clear all; disp('k-means 聚类示例开始!!'); data = GetRawData(); % 获取原始数据的函数 % 使用 k-means 进行聚类 nCluster = 2; % 聚类的数量 [result, means] = kmeansClustering(data, nCluster); % 显示聚类结果 ShowClusteringResult(result, means, nCluster); function [result, means] = kmeansClustering(data, nCluster) % 使用 k-means 进行聚类的方法 % 初始聚类，随机分配类别 result = [data randi(nCluster, [length(data(:,1)), 1])]; while 1 means = []; for i = 1:nCluster % 计算每个聚类的均值 means = [means; mean(result(result(:,3)==i, 1:2))]; end % 显示聚类结果 ShowClusteringResult(result, means, nCluster); % 将每个数据重新聚类，按距离从近到远的顺序 nUpdate = 0; % 更新的聚类数量 for j = 1:length(result(:,1)) % 计算每个数据到每个均值的距离 d = []; for k = 1:nCluster d = [d norm(result(j,1:2)-means(k,:))]; end [c, i] = min(d); % 计算距离最小的聚类的ID if result(j,3) ~= i result(j,3) = i; % 更新聚类 nUpdate = nUpdate + 1; end end if nUpdate == 0 break; % 聚类不再改变时结束 end pause; end function ShowClusteringResult(result, means, nCluster) % 显示聚类结果的函数 hold off; % 为图形创建颜色数据 cc = hsv(nCluster); % 对每个聚类绘制数据 for i = 1:nCluster data = result(result(:,3)==i, 1:2); plot(data(:,1), data(:,2), '.', 'Color', cc(i,:)); hold all; plot(means(i,1), means(i,2), 'x', 'Color', cc(i,:)); hold all; end axis equal; grid on; hold on; function data = GetRawData() % 伪数据的聚类中心和误差量 nSample = [0 0 2; 10 10 5]; ndata = 30; % 每个聚类的数据点数量 data = []; for nc = 1:length(nSample(:,1)) for i = 1:ndata xy = nSample(nc,1:2) + randn(1,2) * nSample(nc,3); data = [data; xy]; end end data = sortrows(data, 2); % 对数据进行排序和混洗