# A Comprehensive Approach to Mode Clustering

arXiv: https://arxiv.org/abs/1406.1780 · HTML: https://mlsanity.com/paper/1406.1780/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2014.

## Built on by (most cited first, 8 of 8)

- [Learning with Density Matrices and Random Features](https://mlsanity.com/paper/2102.04394/) (2021)
- [Space Partitioning and Regression Mode Seeking via a Mean-Shift-Inspired Algorithm](https://mlsanity.com/paper/2104.10103/) (2021)
- [Skeleton Clustering: Dimension-Free Density-based Clustering](https://mlsanity.com/paper/2104.10770/) (2021)
- [Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction](https://mlsanity.com/paper/2110.00317/) (2021)
- [Doc2SAR: A Synergistic Framework for High-Fidelity Extraction of Structure-Activity Relationships from Scientific Documents](https://mlsanity.com/paper/2506.21625/) (2025)
- [Mode and Ridge Estimation in Euclidean and Directional Product Spaces: A Mean Shift Approach](https://mlsanity.com/paper/2110.08505/) (2021)
- [Ridge Estimation with Nonlinear Transformations](https://mlsanity.com/paper/2306.05722/) (2023)
- [Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation](https://mlsanity.com/paper/2606.25927/) (2026)

## Abstract (arXiv)

Mode clustering is a nonparametric method for clustering that defines clusters using the basins of attraction of a density estimator's modes. We provide several enhancements to mode clustering: (i) a soft variant of cluster assignment, (ii) a measure of connectivity between clusters, (iii) a technique for choosing the bandwidth, (iv) a method for denoising small clusters, and (v) an approach to visualizing the clusters. Combining all these enhancements gives us a complete procedure for clustering in multivariate problems. We also compare mode clustering to other clustering methods in several examples