# Poisson Kernel-Based Clustering on the Sphere: Convergence Properties, Identifiability, and a Method of Sampling

arXiv: https://arxiv.org/abs/1803.04485 · HTML: https://mlsanity.com/paper/1803.04485/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [Goodness-of-Fit and Clustering of Spherical Data: the QuadratiK package in R and Python](https://mlsanity.com/paper/2402.02290/) (2024)

## Abstract (arXiv)

Many applications of interest involve data that can be analyzed as unit vectors on a d-dimensional sphere. Specific examples include text mining, in particular clustering of documents, biology, astronomy and medicine among others. Previous work has proposed a clustering method using mixtures of Poisson kernel-based distributions (PKBD) on the sphere. We prove identifiability of mixtures of the aforementioned model, convergence of the associated EM-type algorithm and study its operational characteristics. Furthermore, we propose an empirical densities distance plot for estimating the number of clusters in a PKBD model. Finally, we propose a method to simulate data from Poisson kernel-based densities and exemplify our methods via application on real data sets and simulation experiments.