kmeans聚类算法matlab代码SSE
时间: 2023-09-11 16:09:16 浏览: 72
K-means聚类算法的Matlab代码是:
```matlab
function [idx, C, SSE = kmeans_clustering(X, k, max_iter)
% X: n-by-p data matrix
% k: number of clusters
% max_iter: maximum number of iterations
[n, p = size(X);
C = X(randperm(n, k), :); % Initialize centroids randomly
for iter = 1:max_iter
prev_C = C;
% Assign each data point to the nearest centroid
dist = pdist2(X, C);
[~, idx = min(dist, [], 2);
% Update centroids
for i = 1:k
C(i, :) = mean(X(idx == i, :), 1);
end
% Check convergence
if isequal(C, prev_C)
break;
end
end
% Calculate Sum of Squared Errors (SSE)
SSE = sum(min(dist, [], 2).^2);
end
```
其中,X是n行p列的数据矩阵,k是要划分的簇的数量,max_iter是迭代的最大次数。函数返回idx是长度为n的向量,表示每个数据点所属的簇的索引;C是k行p列的矩阵,表示每个簇的质心;SSE表示聚类结果的平方误差和。
请注意,这只是K-means聚类算法的一种实现方式,不同的实现方式可能会有些许差异。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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