# A review of mean-shift algorithms for clustering

arXiv: https://arxiv.org/abs/1503.00687 · HTML: https://mlsanity.com/paper/1503.00687/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2015.

## Built on by (most cited first, 19 of 19)

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- [Transductive Few-Shot Learning: Clustering is All You Need?](https://mlsanity.com/paper/2106.09516/) (2021)
- [On the number of modes of Gaussian kernel density estimators](https://mlsanity.com/paper/2412.09080/) (2024)
- [Geometric Regularity in Deterministic Sampling Dynamics of Diffusion-based Generative Models](https://mlsanity.com/paper/2506.10177/) (2025)
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- [Skeleton Clustering: Dimension-Free Density-based Clustering](https://mlsanity.com/paper/2104.10770/) (2021)
- [UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters](https://mlsanity.com/paper/2312.11323/) (2023)
- [The EM Perspective of Directional Mean Shift Algorithm](https://mlsanity.com/paper/2101.10058/) (2021)
- [Mode and Ridge Estimation in Euclidean and Directional Product Spaces: A Mean Shift Approach](https://mlsanity.com/paper/2110.08505/) (2021)
- [Structure learning with Temporal Gaussian Mixture for model-based Reinforcement Learning](https://mlsanity.com/paper/2411.11511/) (2024)
- [StructuredMesh: 3D Structured Optimization of Faccade Components on Photogrammetric Mesh Models using Binary Integer Programming](https://mlsanity.com/paper/2306.04184/) (2023)
- [Emergenet: A Digital Twin of Sequence Evolution for Scalable Emergence Risk Assessment of Animal Influenza A Strains](https://mlsanity.com/paper/2411.17154/) (2024)
- [What Does Normal Even Mean? Evaluating Benign Traffic in Intrusion Detection Datasets](https://mlsanity.com/paper/2509.09564/) (2025)
- [Hyperbolic Gaussian Blurring Mean Shift: A Statistical Mode-Seeking Framework for Clustering in Curved Spaces](https://mlsanity.com/paper/2512.11448/) (2025)
- [MoE-TransMov: A Transformer-based Model for Next POI Prediction in Familiar & Unfamiliar Movements](https://mlsanity.com/paper/2512.17985/) (2025)
- [Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers](https://mlsanity.com/paper/2603.12684/) (2026)
- [Gradient-Guided Density Peak Clustering](https://mlsanity.com/paper/2610.01050/) (2026)

## Abstract (arXiv)

A natural way to characterize the cluster structure of a dataset is by finding regions containing a high density of data. This can be done in a nonparametric way with a kernel density estimate, whose modes and hence clusters can be found using mean-shift algorithms. We describe the theory and practice behind clustering based on kernel density estimates and mean-shift algorithms. We discuss the blurring and non-blurring versions of mean-shift; theoretical results about mean-shift algorithms and Gaussian mixtures; relations with scale-space theory, spectral clustering and other algorithms; extensions to tracking, to manifold and graph data, and to manifold denoising; K-modes and Laplacian K-modes algorithms; acceleration strategies for large datasets; and applications to image segmentation, manifold denoising and multivalued regression.