# A comparison of bandwidth selectors for mean shift clustering

arXiv: https://arxiv.org/abs/1310.7855 · HTML: https://mlsanity.com/paper/1310.7855/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2013.

## Built on by (most cited first, 2 of 2)

- [Mode and Ridge Estimation in Euclidean and Directional Product Spaces: A Mean Shift Approach](https://mlsanity.com/paper/2110.08505/) (2021)
- [Bandwidth Selectors on Semiparametric Bayesian Networks](https://mlsanity.com/paper/2506.16844/) (2025)

## Abstract (arXiv)

We explore the performance of several automatic bandwidth selectors, originally designed for density gradient estimation, as data-based procedures for nonparametric, modal clustering. The key tool to obtain a clustering from density gradient estimators is the mean shift algorithm, which allows to obtain a partition not only of the data sample, but also of the whole space. The results of our simulation study suggest that most of the methods considered here, like cross validation and plug in bandwidth selectors, are useful for cluster analysis via the mean shift algorithm.