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This is a Quantitative Study study.

Adaptive clustering using enhanced DBSCAN: a dynamic approach to optimizing density-based clustering

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This study introduces Adaptive DBSCAN for improved clustering quality, highlighting density variations and feature overlaps.

Enhanced ADBSCAN showed notable improvements in clustering quality across three benchmark datasets.
The algorithm dynamically adjusts the epsilon parameter with silhouette score validation for better performance.
Performance issues arise in datasets with high-density variations and significant feature overlaps.
Selecting appropriate parameters is crucial for effective clustering as demonstrated by the Enhanced ADBSCAN model.

Aljibawi et al. (2025) studied this question.

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