kmeans_varpar(X,k)

Implementation of K-means with Variance Partitioning initialization

Version 1.0.1.0 (2,92 KB)
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9. Aug 2017

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Implementation of K-means with Variance Partitioning initialization. Variance Partitioning initialization is a deterministic way of initializing the data centroids, thus producing results that are repeatable and reproducible, without having to resort to tricks like seeding the pseudorandom number generator.

Zitieren als

Stefan Philippo Pszczolkowski Parraguez (2026). kmeans_varpar(X,k) (https://de.mathworks.com/matlabcentral/fileexchange/57229-kmeans_varpar-x-k), MATLAB Central File Exchange. Abgerufen .

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Inspiriert von: k-means++

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1.0.1.0

Removed loop that made sure that the number of returned centrers is equal to the specified k. This is arguably not necessary and since variance partitioning provides a deterministic result, there is potential for getting trapped in an infinite loop.
Removed loop that made sure that the number of returned centrers is equal to the specified k. This is arguably not necessary and since variance partitioning provides a deterministic result, there is potential for getting trapped in an infinite loop.
1.0.0.0