  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/.
