kmeans++聚类算法matlab代码
时间: 2023-08-13 18:08:28 浏览: 56
Sure! Here is an example of K-means++ clustering algorithm implemented in MATLAB:
```matlab
function [labels, centers] = kmeans_plusplus(X, K)
% Initialize the first center randomly
centers = X(randi(size(X, 1)), :);
% Select remaining centers using the K-means++ algorithm
for i = 2:K
distances = pdist2(X, centers, 'squaredeuclidean');
minDistances = min(distances, [], 2);
totalMinDistance = sum(minDistances);
probabilities = minDistances / totalMinDistance;
cumulativeProbs = cumsum(probabilities);
r = rand();
selectedIndex = find(cumulativeProbs >= r, 1);
centers = [centers; X(selectedIndex, :)];
end
% Perform the K-means clustering using the selected centers
labels = kmeans(X, K, 'Start', centers);
end
```
You can use this function by providing your data matrix `X` and the desired number of clusters `K`. It will return the cluster labels and the final cluster centers.
Please note that this implementation assumes that you have the Statistics and Machine Learning Toolbox installed in MATLAB, as it uses the built-in `kmeans` function for the actual clustering step.
Hope this helps! Let me know if you have any further questions.