MFGB-DBSCAN: a multi-factor granular ball-based density clustering algorithm with adaptive parameter optimization
TL;DR
Experiments show that MFGB-DBSCAN achieves competitive clustering accuracy and efficiency compared with representative baselines, particularly on datasets with varying densities and complex structures.
Abstract
Density-based clustering algorithms such as DBSCAN can identify clusters with arbitrary shapes, but their performance is often limited by global parameter sensitivity and high computational cost on complex datasets. Granular-ball-based methods improve efficiency by replacing point-level operations with coarse-grained hyperspherical units, yet existing GB-DBSCAN-style approaches still suffer from limited adaptivity, unreliable granular-ball fusion, and ambiguous boundary assignment. To address these issues, this paper proposes MFGB-DBSCAN, a multi-factor granular-ball-based density clustering algorithm with adaptive parameter optimization. The proposed method follows a reliability-aware coarse-to-fine strategy. First, adaptive parameter initialization estimates key neighborhood and connection scales from data distribution characteristics. Second, a quality-driven granular-ball evaluation mechanism integrates density, cohesion, and separation to distinguish reliable structural units from uncertain boundary regions. Third, a multi-factor fusion strategy combines spatial proximity, density consistency, quality alignment, geometric overlap, and cohesion compatibility to construct cluster skeletons. Finally, a confidence-based two-stage label propagation mechanism refines the assignment of boundary samples. Experiments on synthetic and real-world datasets show that MFGB-DBSCAN achieves competitive clustering accuracy and efficiency compared with representative baselines, particularly on datasets with varying densities and complex structures. The results suggest that MFGB-DBSCAN provides a practical and reliability-aware improvement for granular-ball-based density clustering.