# Spherical Wards clustering and generalized Voronoi diagrams

arXiv: https://arxiv.org/abs/1705.02232 · HTML: https://mlsanity.com/paper/1705.02232/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2017.

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

- [Face Identity-Aware Disentanglement in StyleGAN](https://mlsanity.com/paper/2309.12033/) (2023)

## Abstract (arXiv)

Gaussian mixture model is very useful in many practical problems. Nevertheless, it cannot be directly generalized to non Euclidean spaces. To overcome this problem we present a spherical Gaussian-based clustering approach for partitioning data sets with respect to arbitrary dissimilarity measure. The proposed method is a combination of spherical Cross-Entropy Clustering with a generalized Wards approach. The algorithm finds the optimal number of clusters by automatically removing groups which carry no information. Moreover, it is scale invariant and allows for forming of spherically-shaped clusters of arbitrary sizes. In order to graphically represent and interpret the results the notion of Voronoi diagram was generalized to non Euclidean spaces and applied for introduced clustering method.