# Learned Accelerator Framework for Angular-Distance-Based High-Dimensional DBSCAN

arXiv: https://arxiv.org/abs/2302.03136 · HTML: https://mlsanity.com/paper/2302.03136/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [Deep Lattice Networks and Partial Monotonic Functions](https://mlsanity.com/paper/1709.06680/) (2017)
- [Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval](https://mlsanity.com/paper/2106.11251/) (2021)
- [Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval](https://mlsanity.com/paper/2110.05789/) (2021)
- [DBSCAN++: Towards fast and scalable density clustering](https://mlsanity.com/paper/1810.13105/) (2018)
- [LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense Passage Retrieval](https://mlsanity.com/paper/2205.00970/) (2022)
- [Faster DBSCAN via subsampled similarity queries](https://mlsanity.com/paper/2006.06743/) (2020)
- [Consistent and Flexible Selectivity Estimation for High-Dimensional Data](https://mlsanity.com/paper/2005.09908/) (2020)
- [Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach](https://mlsanity.com/paper/2002.06442/) (2020)
- [A Learned Index for Exact Similarity Search in Metric Spaces](https://mlsanity.com/paper/2204.10028/) (2022)
- [Fast Density Estimation for Density-based Clustering Methods](https://mlsanity.com/paper/2109.11383/) (2021)

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

- [Xling: A Learned Filter Framework for Accelerating High-Dimensional Approximate Similarity Join](https://mlsanity.com/paper/2402.13397/) (2024)

## Abstract (arXiv)

Density-based clustering is a commonly used tool in data science. Today many data science works are utilizing high-dimensional neural embeddings. However, traditional density-based clustering techniques like DBSCAN have a degraded performance on high-dimensional data. In this paper, we propose LAF, a generic learned accelerator framework to speed up the original DBSCAN and the sampling-based variants of DBSCAN on high-dimensional data with angular distance metric. This framework consists of a learned cardinality estimator and a post-processing module. The cardinality estimator can fast predict whether a data point is core or not to skip unnecessary range queries, while the post-processing module detects the false negative predictions and merges the falsely separated clusters. The evaluation shows our LAF-enhanced DBSCAN method outperforms the state-of-the-art efficient DBSCAN variants on both efficiency and quality.