Data clustering method based on stable natural neighborhood-based granular ball generation

Purpose The existing Granular-Ball (GB) generation methods construct granular-balls for clustering tasks by determining their center locations and sizes according to the local distribution characteristics of the data, making it difficult to accurately characterize clusters with arbitrary shapes. Consequently, the clustering performance is constrained by the quality of the generated granular-balls. To address the above issue, this paper proposes a Stable Natural Neighborhood-based Granular Ball (SNGB) generation model. Design/methodology/approach This paper develops an adaptive granular-ball generation method for clustering. The proposed method automatically identifies neighbor core vertex from natural neighborhood relationships, constructs the structure through a core domain member assignment mechanism, and introduces an outlier identification and assignment strategy to balance coverage and compactness. The proposed method is integrated with clustering algorithms, and experiments are conducted on various datasets to evaluate its performance. Findings Experimental results demonstrate that SNGB is effective, efficient, and robust in generating high-quality granular-balls. Compared with existing granular-ball generation methods, SNGB more accurately fits complex cluster shapes and adapts to diverse data distributions, thereby improving clustering performance. Originality/value This paper proposes a granular-ball generation model based on the natural neighborhood structure. Building on natural neighborhood density and representative-point construction methods, the model transforms natural neighborhood representative structures into granular-ball representations and incorporates an outlier assignment strategy, thereby enhancing the capability of granular-balls to characterize data distribution structures. The model exhibits strong stability and robustness in scenarios involving complex structures and noise interference, providing a reliable initialization scheme for granular-ball-based clustering methods.