SACA: Selective Attention-Based Clustering Algorithm

2025-08-23

AI Summary

Density-based clustering algorithms such as DBSCAN suffer from sensitivity to manually tuned parameters and reliance on domain expertise. To address this, we propose Selective-Attention DBSCAN (SA-DBSCAN), an adaptive density clustering method that requires only a single, easily interpretable integer parameter—the neighborhood size *k*. SA-DBSCAN employs an attention-weighted density estimator and an adaptive thresholding mechanism to automatically distinguish sparse points and noise. Following initial clustering, it incorporates a neighborhood expansion and point reintegration strategy to assign previously excluded points to appropriate clusters. Our key innovation lies in integrating selective attention into density clustering, unifying parameter simplification with structural awareness. Extensive experiments on diverse benchmark datasets demonstrate that SA-DBSCAN achieves superior robustness and clustering accuracy while significantly reducing dependence on prior knowledge, thereby enhancing practicality and cross-domain transferability.

Technology Category

• Machine Learning: Clustering
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Clustering algorithms are widely used in various applications, with density-based methods such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN) being particularly prominent. These algorithms identify clusters in high-density regions while treating sparser areas as noise. However, reliance on user-defined parameters often poses optimization challenges that require domain expertise. This paper presents a novel density-based clustering method inspired by the concept of selective attention, which minimizes the need for user-defined parameters under standard conditions. Initially, the algorithm operates without requiring user-defined parameters. If parameter adjustment is needed, the method simplifies the process by introducing a single integer parameter that is straightforward to tune. The approach computes a threshold to filter out the most sparsely distributed points and outliers, forms a preliminary cluster structure, and then reintegrates the excluded points to finalize the results. Experimental evaluations on diverse data sets highlight the accessibility and robust performance of the method, providing an effective alternative for density-based clustering tasks.

Problem

• Reduces reliance on user-defined parameters in clustering
• Automates density-based clustering with selective attention concept
• Simplifies parameter tuning while maintaining robust performance

Innovation

• Selective attention-based clustering algorithm
• Minimizes user-defined parameters requirement
• Single integer parameter simplifies tuning