SACA: Selective Attention-Based Clustering Algorithm

TL;DR

This paper presents SACA, a density-based clustering algorithm inspired by selective attention, which adaptively identifies clusters with minimal parameter tuning, demonstrating robustness and accuracy across various datasets.

Contribution

Introduces SACA, a novel density-based clustering method that reduces parameter dependence through adaptive thresholding and selective point reintegration.

Findings

Robust clustering performance across benchmark datasets

Reduced need for parameter tuning compared to traditional methods

High accuracy and noise handling capabilities

Abstract

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.