K-means算法聚类分析的MATLAB代码

以下是K-means算法聚类分析的MATLAB代码示例：

% 生成待聚类数据
X = [randn(100,2)*0.75+ones(100,2);
     randn(100,2)*0.5-ones(100,2)];

% 初始聚类中心
initial_centroids = [X(1,:); X(2,:)];

% 运行K-means算法
K = size(initial_centroids, 1);
max_iters = 10;
[centroids, idx] = runkMeans(X, initial_centroids, max_iters);

% 可视化聚类结果
figure;
plotDataPoints(X, idx, K);
title('K-means Clustering');

% K-means算法实现
function [centroids, idx] = runkMeans(X, initial_centroids, max_iters)
    % 初始化变量
    [m, n] = size(X);
    K = size(initial_centroids, 1);
    centroids = initial_centroids;
    previous_centroids = centroids;
    idx = zeros(m, 1);

    % 迭代更新聚类中心
    for i=1:max_iters
        fprintf('K-means iteration %d/%d...\n', i, max_iters);
        idx = findClosestCentroids(X, centroids);
        centroids = computeCentroids(X, idx, K);
        if isequal(previous_centroids, centroids)
            break;
        end
        previous_centroids = centroids;
    end
end

% 计算每个样本点距离哪个聚类中心最近
function idx = findClosestCentroids(X, centroids)
    K = size(centroids, 1);
    idx = zeros(size(X,1), 1);
    for i=1:size(X,1)
        distances = sum((X(i,:) - centroids).^2, 2);
        [min_distance, idx(i)] = min(distances);
    end
end

% 计算每个聚类的新中心
function centroids = computeCentroids(X, idx, K)
    [m n] = size(X);
    centroids = zeros(K, n);
    for i=1:K
        indices = find(idx == i);
        centroids(i,:) = mean(X(indices,:));
    end
end

% 绘制聚类结果
function plotDataPoints(X, idx, K)
    colors = hsv(K);
    for i=1:K
        plot(X(idx==i,1), X(idx==i,2), '.', 'color', colors(i,:));
        hold on;
    end
end

该代码生成一个随机的二维数据集，并使用K-means算法将其分为两个簇。聚类结果可视化如下图所示：