用matlab写一段k均值聚类分析代码

Sure, here's an example code snippet for K-means clustering analysis using MATLAB:

% Generate random data
data = randn(100, 2);

% Set number of clusters
k = 3;

% Initialize centroids randomly
centroids = data(randperm(size(data, 1), k), :);

% Set maximum number of iterations and convergence threshold
maxIters = 100;
convergenceThreshold = 0.01;

% Iterate until convergence or maximum number of iterations is reached
for iter = 1:maxIters
% Assign points to closest centroid
[~, clusterAssignments] = pdist2(centroids, data, 'euclidean', 'Smallest', 1);

% Compute new centroids from assigned points
for i = 1:k
centroids(i, :) = mean(data(clusterAssignments == i, :), 1);
end

% Check for convergence
if max(pdist2(centroids, mean(centroids, 1), 'euclidean', 'Smallest', 1)) < convergenceThreshold
break;
end
end

% Plot data points with different colors for different clusters
colors = {'r', 'g', 'b', 'y', 'c', 'm'};
figure();
hold on;
for i = 1:k
scatter(data(clusterAssignments == i, 1), data(clusterAssignments == i, 2), [], colors{i});
end
scatter(centroids(:, 1), centroids(:, 2), 100, 'k', 'X');
hold off;

I hope this helps you with your clustering analysis in MATLAB. Let me know if you have any other questions!