[2603.05067] Synchronization-based clustering on the unit hypersphere

Computer Science > Machine Learning arXiv:2603.05067 (cs) [Submitted on 5 Mar 2026] Title:Synchronization-based clustering on the unit hypersphere Authors:Zinaid Kapić, Aladin Crnkić, Goran Mauša View a PDF of the paper titled Synchronization-based clustering on the unit hypersphere, by Zinaid Kapi\'c and 2 other authors View PDF HTML (experimental) Abstract: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 $\mathbf{S}^{d-1}$. 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. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.05067 [cs.LG]   (or arXiv:2603.05067v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.05067 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: U.P.B. Sci. Bull., Series C, Vol. 88, Iss. 1, 2026 ISSN 2286-3540 Submission history From: Zinaid Kapić [view email] [v1] Thu,...