Multiview clustering (MVC) segregates data samples into meaningful clusters by synthesizing information across multiple views. Moreover, deep learning-based methods have demonstrated their strong feature learning capabilities in MVC scenarios. However, effectively generalizing feature representations while maintaining consistency is still an intractable problem. In addition, most existing deep clustering methods based on contrastive learning overlook the consistency of the clustering representations during the clustering process. In this paper, we show how the above problems can be overcome and propose a consistent enhancement-based deep MVC method via contrastive learning (CCEC). Specifically, semantic connection blocks are incorporated into a feature representation to preserve the consistent information among multiple views. Furthermore, the representation process for clustering is enhanced through spectral clustering, and the consistency across multiple views is improved. Experiments conducted on five datasets demonstrate the effectiveness and superiority of our method in comparison with the state-of-the-art (SOTA) methods. The code for this method can be accessed at https://anonymous.4open.science/r/CCEC-E84E/.

翻译：多视图聚类（MVC）通过综合多个视图的信息将数据样本划分为有意义的簇。近年来，基于深度学习的方法在MVC场景中展现出强大的特征学习能力。然而，如何在保持一致性的同时有效泛化特征表示仍是一个难题。此外，现有基于对比学习的深度聚类方法大多忽视了聚类过程中聚类表示的一致性。本文展示了如何克服上述问题，并提出了一种基于对比学习的一致性增强深度多视图聚类方法（CCEC）。具体而言，在特征表示中引入语义连接模块以保留多视图间的一致性信息；进一步通过谱聚类增强聚类表示过程，并提升多视图间的一致性。在五个数据集上的实验表明，与最先进方法（SOTA）相比，本方法具有有效性和优越性。该方法代码可访问 https://anonymous.4open.science/r/CCEC-E84E/。