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A comment on E-means: 10x faster k-means at equal loss.

Re:E-means: 10x faster k-means at equal loss by hubertzhang on Tuesday November 10, 2026 @02:50AM (#50437302)
what's the common use case for e-means/k-means nowadays?
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Re:E-means: 10x faster k-means at equal loss by swann_bessa on Tuesday November 10, 2026 @10:53AM (#50442134)
Minimizing Mean Squared Error between a dataset and a given number of centroids. In practice it can be used in deduplication, data curation, compression, Vector Search, and many others. It remains one of the most used Machine Learning algorithms in the world.
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