GRANITE: Granular-Ball Autoencoding with Signed Triplet Evidence for Uncertainty-Aware Deep Clustering

Clustering is a fundamental task in data mining and plays an important role in complex data analysis. Traditional single-granularity instance-level clustering methods treat samples as isolated points, limiting their ability to learn multi-scale representations. Granular-ball(GB) clustering obtains multi-granularity representations through spherical topology evolution, making it more suitable for handling complex data. However, existing GB clustering methods often rely on fixed feature and overlook uncertainty in ball–cluster assignments. To address them, we propose the Granular-Ball Autoencoding with Intuitionistic Triplet Evidence (GRANITE) algorithm for uncertainty-aware deep clustering. A GB autoencoder is designed to learn dynamic deep feature representations in the latent space. In addition, to explicitly characterize the uncertainty in ball–cluster relationships, we introduce intuitionistic fuzzy triplet evidence comprising membership, non-membership, and hesitation degrees. Furthermore, GRANITE employs a new hesitation evidence and introduces the GB distribution measure to regularize the clustering process, thereby improving robustness and efficiency. Experiments on multiple benchmark datasets demonstrate that GRANITE achieves superior clustering performance compared with existing methods.