Functional clustering on a sphere via Riemannian functional principal components

Abstract

We propose the functional clustering algorithm applicable to the sphere‐valued random curves, called ‐centres Riemannian functional clustering (kCRFC). It is based on Riemannian functional principal component scores and ‐centres functional clustering algorithm; thus, we can obtain accurate clustering results by reflecting the geometry of the sphere. Our method shows better clustering performances than existing multivariate functional clustering methods in various simulation settings. We apply the proposed method to the migration trajectories of Egyptian Vultures in the Middle East and East Africa and fruit fly behaviours, containing the curves lied on two‐dimensional and three‐dimensional sphere, respectively.

Study on Awareness of Suicide and Suicide Prevention Among Community Youth

“…According to a survey on SNS usage behavior conducted in 2021, 89.7% of individuals in their 20s were the most frequent users of social media (89.7%). Therefore, there is a possibility of concomitant changes in awareness regarding suicide because of media influence [ 35 ]. However, it is also suggested that these social media platforms are good spaces for seeking help and receiving support from others [ 28 ].…”