# Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm

arXiv: https://arxiv.org/abs/2012.10929 · HTML: https://mlsanity.com/paper/2012.10929/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [End-to-end Differentiable Clustering with Associative Memories](https://mlsanity.com/paper/2306.03209/) (2023)
- [Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification](https://mlsanity.com/paper/2106.08590/) (2021)
- [PaVa: a novel Path-based Valley-seeking clustering algorithm](https://mlsanity.com/paper/2306.07503/) (2023)
- [Stochastic mean-shift clustering](https://mlsanity.com/paper/2312.15684/) (2023)
- [Hyperbolic Gaussian Blurring Mean Shift: A Statistical Mode-Seeking Framework for Clustering in Curved Spaces](https://mlsanity.com/paper/2512.11448/) (2025)

## Abstract (arXiv)

Mean shift is a simple interactive procedure that gradually shifts data points towards the mode which denotes the highest density of data points in the region. Mean shift algorithms have been effectively used for data denoising, mode seeking, and finding the number of clusters in a dataset in an automated fashion. However, the merits of mean shift quickly fade away as the data dimensions increase and only a handful of features contain useful information about the cluster structure of the data. We propose a simple yet elegant feature-weighted variant of mean shift to efficiently learn the feature importance and thus, extending the merits of mean shift to high-dimensional data. The resulting algorithm not only outperforms the conventional mean shift clustering procedure but also preserves its computational simplicity. In addition, the proposed method comes with rigorous theoretical convergence guarantees and a convergence rate of at least a cubic order. The efficacy of our proposal is thoroughly assessed through experimental comparison against baseline and state-of-the-art clustering methods on synthetic as well as real-world datasets.