Deep Fair Multi-View Clustering with Attention KAN

Abstract

Multi-view clustering is effective in unsupervised multi-view data analysis and has received considerable attention. However, most existing methods excessively emphasize certain attributes, resulting in unfair clustering outcomes, i.e., certain sensitive attributes dominate the clustering results. Moreover, existing methods struggle to effectively capture complex nonlinear relationships and interactions across views, limiting their ability to achieve optimal clustering performance. Therefore, in this work, we propose a novel method, Deep Fair Multi-View Clustering with Attention Kolmogorov-Arnold Network (DFMVC-AKAN), to generate fair clustering results while maintaining robust performance. DFMVC-AKAN integrates attention mechanisms into Kolmogorov-Arnold Networks (KAN) to exploit the complex nonlinear inter-view relationships. Specifically, KAN provides a nonlinear feature representation capable of efficiently approximating arbitrary multivariate continuous functions, augmented by a hybrid attention mechanism which enables the model to dynamically focus on the most relevant features. Finally, we refine the clustering assignments with a distribution alignment module to ensure fair outcomes across diverse groups while maintaining discriminative ability. Experimental results on four datasets containing sensitive attributes demonstrate that DFMVC-AKAN significantly improves fairness and clustering performance compared to state-of-the-art methods.