Multi-view clustering aims to integrate complementary information from multiple views to achieve better performance than single-view clustering. However, in practical scenarios, the quality of each view is often inconsistent, with some views containing substantial noise or redundant information, which may adversely affect the overall clustering performance. Moreover, existing contrastive learning techniques typically employ overly simplistic strategies for negative sample selection, making them prone to local optima during training and compromising model effectiveness. To address these challenges, this paper proposes a novel information fusion-based deep multi-view contrastive clustering algorithm, termed ECMVC. The proposed method explicitly models both consistency and complementarity among views and leverages a feature fusion network to enhance the stability and accuracy of clustering in noisy and redundant environments. In addition, we further propose a curriculum-guided contrastive learning approach, where an entropy-driven dynamic scheduler adaptively selects informative negative samples and progressively increases the training difficulty. This curriculum-guided mechanism enables faster convergence and more stable optimization. Experiments on multiple benchmark datasets demonstrate the effectiveness of the proposed method.
Ma et al. (2026) studied this question.