  Clustering is a fundamental unsupervised representation learning task with
wide application in computer vision and pattern recognition. Deep clustering
utilizes deep neural networks to learn latent representation, which is suitable
for clustering. However, previous deep clustering methods, especially image
clustering, focus on the features of the data itself and ignore the
relationship between the data, which is crucial for clustering. In this paper,
we propose a novel Deep Structure and Attention aware Subspace Clustering
(DSASC), which simultaneously considers data content and structure information.
We use a vision transformer to extract features, and the extracted features are
divided into two parts, structure features, and content features. The two
features are used to learn a more efficient subspace structure for spectral
clustering. Extensive experimental results demonstrate that our method
significantly outperforms state-of-the-art methods. Our code will be available
at https://github.com/cs-whh/DSASC
