# ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities

arXiv: https://arxiv.org/abs/1809.06189 · HTML: https://mlsanity.com/paper/1809.06189/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [A3S: A General Active Clustering Method with Pairwise Constraints](https://mlsanity.com/paper/2407.10196/) (2024)
- [SDC-HSDD-NDSA: Structure Detecting Cluster by Hierarchical Secondary Directed Differential with Normalized Density and Self-Adaption](https://mlsanity.com/paper/2307.00677/) (2023)
- [HAECcity: Open-Vocabulary Scene Understanding of City-Scale Point Clouds with Superpoint Graph Clustering](https://mlsanity.com/paper/2504.13590/) (2025)
- [Hazy Pedestrian Trajectory Prediction via Physical Priors and Graph-Mamba](https://mlsanity.com/paper/2509.24020/) (2025)

## Abstract (arXiv)

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced performances for clusters with different densities. Therefore, in this paper, an adaptive DBSCAN is proposed which can work significantly well for identifying clusters with varying densities.