Post
55
Replace k-means clustering algorithm with e-means algorithm
Paper - https://arxiv.org/abs/2611.09501
Lloyd's k-means algorithm is a standard ML algorithm for clustering.
This paper introduces e-means clustering algorithm which is
[1] much faster than Lloyd k-means and
[2] converges to lower losses, frequently even beating k-means++.
On a wide array of embeddings datasets, it beats the loss of Lloyd k-means at 25 epochs in 1.4 to 2.6 epochs,
As an example, e-means partitions DINO10B, a dataset of 10 billion DINO embeddings, into 16 million clusters in only 2.5 epochs, reaching a loss below Lloyd's at 25 epochs.
Paper - https://arxiv.org/abs/2611.09501
Lloyd's k-means algorithm is a standard ML algorithm for clustering.
This paper introduces e-means clustering algorithm which is
[1] much faster than Lloyd k-means and
[2] converges to lower losses, frequently even beating k-means++.
On a wide array of embeddings datasets, it beats the loss of Lloyd k-means at 25 epochs in 1.4 to 2.6 epochs,
As an example, e-means partitions DINO10B, a dataset of 10 billion DINO embeddings, into 16 million clusters in only 2.5 epochs, reaching a loss below Lloyd's at 25 epochs.
