K-means++ clustering for classification
K-means clustering algorithm implementation with k-means++ augmentation.
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K-means++ augmentation improves both the speed and the accuracy of the k-means clustering algorithm.
The Matlab code of the algorithm is provided together with a simple elbow searching function to get the minimum of the objective function when the slope of the curve becomes less than 2%.
As a stopping criterion for the clustering algorithm, a threshold on the relative difference of the values of the objective function compared to the best found has been considered.
인용 양식
Claudio Fontana (2026). K-means++ clustering for classification (https://kr.mathworks.com/matlabcentral/fileexchange/112485-k-means-clustering-for-classification), MATLAB Central File Exchange. 검색 날짜: .
