LLM-DAMVC: A Large Language Model Guided Dynamic Agent Multi-View Clustering
HaiMing Xu, QIANQIAN WANG
Multi-view clustering integrates the consistency and complementarity of multiple data sources to achieve unsupervised data segmentation. Existing multi-view clustering methods primarily confront two key challenges: i) excessive reliance on feature extraction in the original feature space, neglecting the global structural information embedded in the frequency domain; ii) adoption of static predefined weights for view fusion, lacking the capability to adaptively adjust strategies according to variations in data distribution and view quality, thus failing to address the differentiated contributions of diverse views to distinct samples. To address these issues, we propose the LLM-DAMVC framework, which employs a large-scale language model for multi-view clustering via a heterogeneous agent federation mechanism. Specifically, each view is equipped with complementary agents dedicated to feature extraction and adversarial learning, respectively. A dual-domain contrast regularization module is introduced to optimize feature consistency in both the feature space and frequency domain. Additionally, a self-attentive feature modulation module is developed to dynamically assess feature quality and implement enhanced multi-head attention mechanisms. Extensive experimental results on multiple benchmark datasets validate the effectiveness and superiority of the proposed method.