E-means: 10x faster k-means at equal loss

Re:E-means: 10x faster k-means at equal loss by refactor_master on Tuesday November 10, 2026 @03:30AM (#50437567)

So this is a different clustering algorithm, of which there are many, maaaany?

That makes it... not very interesting, because it's not a drop-in replacement then, and potentially many other clustering methods would have worked in particular applications as well. Or does this one beat most of the others as well?

[ Parent | on HN ]

Re:E-means: 10x faster k-means at equal loss by swann_bessa on Tuesday November 10, 2026 @05:49AM (#50438496)

It's not a new kind of clustering. It solves exactly the same problem as k-means (minimize the mean squared distance to k centroids), returns the same thing (k centroids and an assignment), and has the same inputs (k and a number of epochs). The batch size and step size are set automatically, so there's nothing extra to tune. So it is a drop-in replacement for k-means specifically: in Faiss it's Emeans(d, k).train(x) where you'd call Kmeans(d, k).train(x). The code is in faiss at https://github.com/facebookresearch/faiss/tree/main/contrib/...

[ Parent | on HN ]