Multi-granularity collaborative clustering based on adaptive granular-balls.

The granular-ball clustering has become a research hotpots in recent years. By leveraging the efficiency of granular-balls in data representation and processing, the performance of traditional clustering methods has been significantly improved. However, existing granular-ball clustering methods still face limitations when dealing with noise points and boundary points between clusters. To address these issues, this paper proposes a novel multi-granularity collaborative clustering based on adaptive granular-balls (AGB-MCC). First, a nearest neighbor method is introduced to generate granular-balls adaptively. Then, the set of granular-balls is divided into high-compactness and low-compactness subsets based on their compactness. At the coarse-granularity level, clustering is performed on the high-compactness granular-balls using an intersection-based principle, and a pruning strategy is employed to optimize the clustering results. Finally, at the fine-granularity level, data points within the low-compactness granular-balls are further clustered based on the shortest-distance criterion to produce the final clustering outcome. Extensive experiments on both synthetic and real-world datasets demonstrate that, compared with the latest granular-ball-based methods, AGB-MCC achieves superior performance in handling noise and overlapping points. Moreover, it exhibits strong adaptability across diverse datasets while maintaining high clustering robustness and computational efficiency. Code: https://github.com/SunnyZCode/AGB-MCC.