k-中心点聚类matlab代码

以下是一个使用MATLAB实现k-medoids聚类算法的示例代码：

function [index_km, center_km = kmedoids(data, cluster_num)
    % 随机选取初始聚类中心点
    center_indices = randperm(size(data, 1), cluster_num);
    center_km = data(center_indices, :);
    % 迭代更新簇的中心点，直到收敛
    while true
        % 分配数据点到最近的中心点所在的簇
        dist_matrix = pdist2(data, center_km);
        [~, index_km = min(dist_matrix, [], 2);
        % 更新簇的中心点
        for i = 1:cluster_num
            cluster_points = data(index_km == i, :);
            cost = sum(pdist2(cluster_points, cluster_points), 'all');
            [~, min_index = min(sum(pdist2(cluster_points, cluster_points), 2));
            center_km(i, :) = cluster_points(min_index, :);
        end
        % 判断是否收敛
        if old_center_km == center_km
            break;
        else
            old_center_km = center_km;
        end
    end
end

这是一个简化的k-medoids聚类算法实现。在该算法中，首先随机选取k个初始聚类中心点，然后迭代更新簇的中心点，直到收敛。收敛的条件是中心点不再发生变化。算法过程中，使用欧氏距离作为相似度的评价指标，将每个数据点分配到距离最近的中心点所在的簇中。

请注意，这只是一个示例代码，可能需要根据实际情况进行修改和优化。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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