# CRAD: Clustering with Robust Autocuts and Depth

arXiv: https://arxiv.org/abs/1904.04020 · HTML: https://mlsanity.com/paper/1904.04020/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Data Depth and Core-based Trend Detection on Blockchain Transaction Networks](https://mlsanity.com/paper/2303.14241/) (2023)
- [SDC-HSDD-NDSA: Structure Detecting Cluster by Hierarchical Secondary Directed Differential with Normalized Density and Self-Adaption](https://mlsanity.com/paper/2307.00677/) (2023)
- [Depth-Based Local Center Clustering: A Framework for Handling Different Clustering Scenarios](https://mlsanity.com/paper/2505.09516/) (2025)

## Abstract (arXiv)

We develop a new density-based clustering algorithm named CRAD which is based on a new neighbor searching function with a robust data depth as the dissimilarity measure. Our experiments prove that the new CRAD is highly competitive at detecting clusters with varying densities, compared with the existing algorithms such as DBSCAN, OPTICS and DBCA. Furthermore, a new effective parameter selection procedure is developed to select the optimal underlying parameter in the real-world clustering, when the ground truth is unknown. Lastly, we suggest a new clustering framework that extends CRAD from spatial data clustering to time series clustering without a-priori knowledge of the true number of clusters. The performance of CRAD is evaluated through extensive experimental studies.