Enhanced One-Step Incomplete Multiview Fuzzy Clustering With Dual Representation Learning

Multi-view fuzzy clustering has attracted increasing attention owing to its strong clustering performance and inherent ability to effectively model uncertainty. However, most existing methods rely on the unrealistic assumption that all views are fully observed, which rarely holds in practice. Although several methods have been proposed to address incomplete multi-view data, they typically focus only on extracting shared information across views while overlooking view-specific information. Moreover, they often tend to neglect missing views imputation, a key mechanism for handling incomplete data. Furthermore, by separating representation learning from clustering, many existing frameworks yield representations that are not necessarily optimal for clustering, thus compromising robustness. To address these limitations and based on fuzzy clustering, a novel enhanced one step incomplete multi-view clustering method (IMVFCM_DRL) is proposed in this paper. First, to effectively handle incomplete multi-view data, we construct a new representation learning framework that explicitly integrates missing-view imputation. Second, to fully exploit the multi-view information, a dual information learning strategy is introduced to jointly capture both common and view-specific information. Finally, a unified one-step fuzzy clustering framework with weighted structure preservation is developed, ensuring that representation learning and fuzzy clustering are jointly optimized. The experiments conducted on various multi-view datasets demonstrate the effectiveness of IMVFCM_DRL. The codes are available at https://github.com/BBKing49/IMVFCM_DRL.