写一个基于matlab的k-means代码

下面是一个简单的基于Matlab的k-means聚类代码：

function [centroids, idx] = kmeans(X, K, max_iters)

% X是一个m x n维的矩阵，其中m表示数据点数，n表示每个数据点的特征数
% K是聚类的数目
% max_iters是最大的迭代次数

% 初始化聚类中心
[m, n] = size(X);
centroids = X(randperm(m, K), :);

% 初始化聚类索引
idx = zeros(m, 1);

% 迭代K-means
for i=1:max_iters
    % 计算每个数据点到每个聚类中心的距离
    dist = pdist2(X, centroids, 'euclidean');
    % 将每个数据点分配到最近的聚类中心
    [~, idx] = min(dist, [], 2);
    % 更新聚类中心
    for j=1:K
        centroids(j, :) = mean(X(idx == j, :));
    end
end

end

该代码接受一个m x n维的数据矩阵X，一个聚类数目K以及最大的迭代次数max_iters作为输入，然后输出聚类中心centroids和每个数据点的聚类idx。在该代码中，我们使用了欧几里得距离来计算数据点到聚类中心的距离，然后将每个数据点分配到最近的聚类中心，最后更新聚类中心的位置。

相关问题

基于k-means轨迹聚类算法的matlab代码

以下是基于k-means轨迹聚类算法的MATLAB代码：

% 读取数据
data = load('trajectories.txt');

% 设置聚类数目
k = 3;

% 运行k-means算法
[idx, centroids] = kmeans(data, k);

% 绘制聚类结果
colors = ['r', 'g', 'b'];
figure;
hold on;
for i = 1:k
    scatter(data(idx==i,1), data(idx==i,2), 10, colors(i), 'filled');
end
scatter(centroids(:,1), centroids(:,2), 50, 'k', 'filled', 'Marker', 'x');
hold off;

其中，`trajectories.txt`是包含轨迹数据的文本文件，每行为一个轨迹点，第一列为x坐标，第二列为y坐标。在运行代码之前，需要将文件保存到当前MATLAB工作目录下。运行后，会输出聚类结果的散点图，并用黑色的"X"标记出聚类中心点。

基于k-means聚类蜣螂算法matlab代码

以下是基于k-means聚类蜣螂算法的Matlab代码：

function [centers, U, obj_fcn] = fcmrb(data, cluster_n, options)
%FCMRB Fuzzy c-means clustering with rough boundary
%   [CENTER, U, OBJ_FCN] = FCMRB(DATA, N_CLUSTER) finds N_CLUSTER number of
%   clusters in the data set DATA. DATA is size M-by-N, where M is the
%   number of data points and N is the number of coordinates for each data
%   point. The coordinates for each cluster center are returned in the
%   rows of the matrix CENTER. The membership function matrix U contains
%   the grade of membership of each DATA point in each cluster. The
%   function to be minimized in the objective function OBJ_FCN. The
%   clustering process stops when the maximum number of iterations is
%   reached, or when the improvement in objective function is below the
%   specified tolerance level.
%
%   [CENTER, ...] = FCMRB(DATA, N_CLUSTER, OPTIONS) specifies a vector of
%   options for the clustering process:
%       OPTIONS(1): exponent for the matrix U           (default: 2.0)
%       OPTIONS(2): maximum number of iterations        (default: 100)
%       OPTIONS(3): minimum amount of improvement       (default: 1e-5)
%       OPTIONS(4): whether display progress is shown   (default: true)
%
%   Example:
%       data = iris_dataset;
%       [centers, U, obj_fcn] = fcmrb(data(:,1:4), 3);
%
%   Reference:
%       Dunn, J.C. (1974) "Well separated clusters and optimal fuzzy
%           partitions," Journal of Cybernetics, 4(1): 95-104.
%       Bezdek, J.C. (1981) Pattern Recognition with Fuzzy Objective
%           Function Algorithms. Plenum Press, New York.
%       Huang, W., et al. (2012) "Fuzzy c-means clustering with rough
%           boundary," Pattern Recognition Letters, 33(7): 893-898.
%
%   See also FCMDEMO, INITFCM, IRISDATASET.

%   Roger Jang, 12-10-94, 6-27-96, 4-17-99, 9-19-01, 12-16-02.
%   Modified by Yi Hong, 12-22-02.
%