# k-MLE, k-Bregman, k-VARs: Theory, Convergence, Computation

arXiv: https://arxiv.org/abs/2409.06938 · HTML: https://mlsanity.com/paper/2409.06938/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 45 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features](https://mlsanity.com/paper/1802.01059/) (2018)
- [An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering](https://mlsanity.com/paper/1302.1552/) (2013)

## Abstract (arXiv)

We develop hard clustering based on likelihood rather than distance and prove convergence. We also provide simulations and real data examples.