A single clustering refers to the partitioning of data such that the similar data are assigned into the same group, whereas the dissimilar data are separated into different groups. Recently, multiview clustering has received significant attention in recent years. However, most existing works tackle the single-clustering scenario, which only use single clustering to partition the data. In practice, nevertheless, the real-world data are complex and can be clustered in multiple ways depending on different interpretations of the data. Unlike these methods, in this article, we apply dual clustering to multiview subspace clustering. We propose a multiview dual-clustering method to simultaneously explore consensus representation and dual-clustering structure in a unified framework. First, multiview features are integrated into a latent embedding representation through a multiview learning process. Second, the dual-clustering segmentation is incorporated into the subspace clustering framework. Finally, the learned dual representations are assigned to the corresponding clusterings. The proposed approach is efficiently solved using an alternating optimization scheme. Extensive experiments demonstrate the superiority of our method on real-world multiview dual- and single-clustering datasets.