# Synchronization-based clustering on the unit hypersphere

arXiv: https://arxiv.org/abs/2603.05067 · HTML: https://mlsanity.com/paper/2603.05067/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 3 of 3)

- [Recent advances in directional statistics](https://mlsanity.com/paper/2005.06889/) (2020)
- [A Fast Synchronization Clustering Algorithm](https://mlsanity.com/paper/1407.7449/) (2014)
- [A new dynamical model for solving rotation averaging problem](https://mlsanity.com/paper/2111.11723/) (2021)

## Abstract (arXiv)

Clustering on the unit hypersphere is a fundamental problem in various fields, with applications ranging from gene expression analysis to text and image classification. Traditional clustering methods are not always suitable for unit sphere data, as they do not account for the geometric structure of the sphere. We introduce a novel algorithm for clustering data represented as points on the unit sphere Sd-1. Our method is based on the d-dimensional generalized Kuramoto model. The effectiveness of the introduced method is demonstrated on synthetic and real-world datasets. Results are compared with some of the traditional clustering methods, showing that our method achieves similar or better results in terms of clustering accuracy.