# Enabling DBSCAN for Very Large-Scale High-Dimensional Spaces

arXiv: https://arxiv.org/abs/2411.11421 · HTML: https://mlsanity.com/paper/2411.11421/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 18 Nov 2024.

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

- [Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques](https://mlsanity.com/paper/2505.03848/) (2025)

## Abstract (arXiv)

DBSCAN is one of the most important non-parametric unsupervised data analysis tools. By applying DBSCAN to a dataset, two key analytical results can be obtained: (1) clustering data points based on density distribution and (2) identifying outliers in the dataset. However, the time complexity of the DBSCAN algorithm is O(n2 β), where n is the number of data points and β = O(D), with D representing the dimensionality of the data space. As a result, DBSCAN becomes computationally infeasible when both n and D are large. In this paper, we propose a DBSCAN method based on spectral data compression, capable of efficiently processing datasets with a large number of data points (n) and high dimensionality (D). By preserving only the most critical structural information during the compression process, our method effectively removes substantial redundancy and noise. Consequently, the solution quality of DBSCAN is significantly improved, enabling more accurate and reliable results.