🤖 AI Summary
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.
📝 Abstract
Clustering is an efficient and essential technique for exploring latent knowledge of data. However, limited attention has been given to the interpretability of the clusters detected by most clustering algorithms. In addition, due to the homogeneity of data, different groups of data have their own homogeneous styles. In this paper, the above two aspects are considered, and an interpretable style Takagi-Sugeno-Kang (TSK) fuzzy clustering (IS-TSK-FC) algorithm is proposed. The clustering behavior of IS-TSK-FC is fully guided by the TSK fuzzy inference on fuzzy rules. In particular, samples are grouped into clusters represented by the corresponding consequent vectors of all fuzzy rules learned in an unsupervised manner. This can explain how the clusters are generated in detail, thus making the underlying decision-making process of the IS-TSK-FC interpretable. Moreover, a series of style matrices are introduced to facilitate the consequents of fuzzy rules in IS-TSK-FC by capturing the styles of clusters as well as the nuances between different styles. Consequently, all the fuzzy rules in IS-TSK-FC have powerful data representation capability. After determining the antecedents of all the fuzzy rules, the optimization problem of IS-TSK-FC can be iteratively solved in an alternation manner. The effectiveness of IS-TSK-FC as an interpretable clustering tool is validated through extensive experiments on benchmark datasets with unknown implicit/explicit styles. Specially, the superior clustering performance of IS-TSK-FC is demonstrated on case studies where different groups of data present explicit styles. The source code of IS-TSK-FC can be downloaded from https://github.com/gusuhang10/IS-TSK-FC.