matlabkmeans聚类算法代码

以下是一个简单的 MATLAB 实现的 k-means 聚类算法的代码：

function [index_cluster, cluster = kmeans_func(data, cluster_num)
    [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_max)
        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

这段代码实现了 k-means 聚类算法，通过传入数据和要划分的簇的数量，返回每个数据点所属的簇标签和最终的聚类中心。算法迭代更新聚类中心和重新分配数据点，直到满足结束条件（中心变化小于阈值或达到最大次数）。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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