# Depth-Based Local Center Clustering: A Framework for Handling Different Clustering Scenarios

arXiv: https://arxiv.org/abs/2505.09516 · HTML: https://mlsanity.com/paper/2505.09516/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 92 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 7 of 7)

- [A Mixture of Generalized Hyperbolic Distributions](https://mlsanity.com/paper/1305.1036/) (2013)
- [EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph](https://mlsanity.com/paper/1609.07228/) (2016)
- [Choosing among notions of multivariate depth statistics](https://mlsanity.com/paper/2004.01927/) (2020)
- [Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodel](https://mlsanity.com/paper/2011.04558/) (2020)
- [Clustering strategy and method selection](https://mlsanity.com/paper/1503.02059/) (2015)
- [Flexible High-Dimensional Unsupervised Learning with Missing Data](https://mlsanity.com/paper/1706.06185/) (2017)
- [CRAD: Clustering with Robust Autocuts and Depth](https://mlsanity.com/paper/1904.04020/) (2019)

## Built on by (most cited first, 1 of 1)

- [Automatic depth-based local center clustering via β-integrated local depth and adaptive grouping](https://mlsanity.com/paper/2609.26748/) (2026)

## Abstract (arXiv)

Cluster analysis, or clustering, plays a crucial role across numerous scientific and engineering domains. Despite the wealth of clustering methods proposed over the past decades, each method is typically designed for specific scenarios and presents certain limitations in practical applications. In this paper, we propose depth-based local center clustering (DLCC). This novel method makes use of data depth, which is known to produce a center-outward ordering of sample points in a multivariate space. However, data depth typically fails to capture the multimodal characteristics of {data}, something of the utmost importance in the context of clustering. To overcome this, DLCC makes use of a local version of data depth that is based on subsets of {data}. From this, local centers can be identified as well as clusters of varying shapes. Furthermore, we propose a new internal metric based on density-based clustering to evaluate clustering performance on {non-convex clusters}. Overall, DLCC is a flexible clustering approach that seems to overcome some limitations of traditional clustering methods, thereby enhancing data analysis capabilities across a wide range of application scenarios.