Interpretable Style Takagi-Sugeno-Kang Fuzzy Clustering
Existing clustering methods generally lack interpretability and neglect intrinsic inter-group style heterogeneity and intra-group homogeneity. To address this, we propose an interpretable stylized TSK fuzzy clustering method: it unsupervisedly generates clusters represented by rule consequent vectors, and explicitly models intra-group homogeneity and inter-group stylistic discrepancies via a learnable style matrix—thereby achieving dual interpretability of cluster structure and decision logic. This work is the first to deeply integrate style modeling with Takagi–Sugeno–Kang (TSK) fuzzy inference into unsupervised clustering, enabling adaptive identification of both explicit and implicit data styles. Extensive experiments on diverse benchmark datasets demonstrate that our method significantly outperforms state-of-the-art clustering algorithms, especially in style-sensitive tasks. The source code is publicly available.