Synchronization-based clustering on the unit hypersphere
This paper tackles the problem of accurately clustering data on the unit hypersphere, which is important for researchers and practitioners working with high-dimensional directional data.
This paper addresses the problem of clustering data on the unit hypersphere, which is crucial for applications like gene expression analysis and image classification. The authors introduce a novel algorithm based on the d-dimensional generalized Kuramoto model, demonstrating similar or better clustering accuracy compared to traditional methods on synthetic and real-world datasets.
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.