SACA: Selective Attention-Based Clustering Algorithm
This work addresses parameter optimization challenges in clustering for users in data analysis, though it appears incremental as it builds on existing density-based methods like DBSCAN.
The paper tackles the problem of user-defined parameter reliance in density-based clustering by introducing a novel method inspired by selective attention, which minimizes or simplifies parameter tuning and demonstrates robust performance on diverse datasets.
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.