# Skeleton Clustering: Dimension-Free Density-based Clustering

arXiv: https://arxiv.org/abs/2104.10770 · HTML: https://mlsanity.com/paper/2104.10770/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 80 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 16 of 16)

- [Community Detection and Stochastic Block Models](https://mlsanity.com/paper/1703.10146/) (2017)
- [A Tutorial on Kernel Density Estimation and Recent Advances](https://mlsanity.com/paper/1704.03924/) (2017)
- [Practical Coreset Constructions for Machine Learning](https://mlsanity.com/paper/1703.06476/) (2017)
- [A review of mean-shift algorithms for clustering](https://mlsanity.com/paper/1503.00687/) (2015)
- [Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering](https://mlsanity.com/paper/1802.04397/) (2018)
- [A Population Background for Nonparametric Density-Based Clustering](https://mlsanity.com/paper/1408.1381/) (2014)
- [Consistent procedures for cluster tree estimation and pruning](https://mlsanity.com/paper/1406.1546/) (2014)
- [A Comprehensive Approach to Mode Clustering](https://mlsanity.com/paper/1406.1780/) (2014)
- [Mixture model modal clustering](https://mlsanity.com/paper/1609.04721/) (2016)
- [Merging K-means with hierarchical clustering for identifying general-shaped groups](https://mlsanity.com/paper/1712.08786/) (2017)
- [A Survey on Theoretical Advances of Community Detection in Networks](https://mlsanity.com/paper/1809.07691/) (2018)
- [A Statistical Perspective on Coreset Density Estimation](https://mlsanity.com/paper/2011.04907/) (2020)
- [Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering](https://mlsanity.com/paper/1506.06422/) (2015)
- [Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering](https://mlsanity.com/paper/1805.09505/) (2018)
- [Statistical Inference for Cluster Trees](https://mlsanity.com/paper/1605.06416/) (2016)
- [Predictive clustering](https://mlsanity.com/paper/1903.08125/) (2019)

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

- [Skeleton Regression: A Graph-Based Approach to Estimation with Manifold Structure](https://mlsanity.com/paper/2303.11786/) (2023)
- [CavMerge: Merging K-means Based on Local Log-Concavity](https://mlsanity.com/paper/2604.04302/) (2026)

## Abstract (arXiv)

We introduce a density-based clustering method called skeleton clustering that can detect clusters in multivariate and even high-dimensional data with irregular shapes. To bypass the curse of dimensionality, we propose surrogate density measures that are less dependent on the dimension but have intuitive geometric interpretations. The clustering framework constructs a concise representation of the given data as an intermediate step and can be thought of as a combination of prototype methods, density-based clustering, and hierarchical clustering. We show by theoretical analysis and empirical studies that the skeleton clustering leads to reliable clusters in multivariate and high-dimensional scenarios.