# SACA: Selective Attention-Based Clustering Algorithm

arXiv: https://arxiv.org/abs/2508.17150 · HTML: https://mlsanity.com/paper/2508.17150/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 46 references that are in the map.
- First posted on arXiv 23 Aug 2025.

## Builds on (most cited first, 1 of 1)

- [Theoretically-Efficient and Practical Parallel DBSCAN](https://mlsanity.com/paper/1912.06255/) (2019)

## Abstract (arXiv)

Clustering algorithms are fundamental tools across many fields, with density-based methods offering particular advantages in identifying arbitrarily shaped clusters and handling noise. However, their effectiveness is often limited by the requirement of critical parameter tuning by users, which typically requires significant domain expertise. This paper introduces a novel density-based clustering algorithm loosely inspired by the concept of selective attention, designed to minimize reliance on parameter tuning for most applications. The proposed method computes an adaptive threshold to exclude sparsely distributed points and outliers, constructs an initial cluster framework, and subsequently reintegrates the filtered points to refine the final results. Extensive experiments on diverse benchmark datasets demonstrate the robustness, accuracy, and ease of use of the proposed approach, establishing it as a powerful alternative to conventional density-based clustering techniques.