  Deep clustering is a fundamental yet challenging task for data analysis.
Recently we witness a strong tendency of combining autoencoder and graph neural
networks to exploit structure information for clustering performance
enhancement. However, we observe that existing literature 1) lacks a dynamic
fusion mechanism to selectively integrate and refine the information of graph
structure and node attributes for consensus representation learning; 2) fails
to extract information from both sides for robust target distribution (i.e.,
"groundtruth" soft labels) generation. To tackle the above issues, we propose a
Deep Fusion Clustering Network (DFCN). Specifically, in our network, an
interdependency learning-based Structure and Attribute Information Fusion
(SAIF) module is proposed to explicitly merge the representations learned by an
autoencoder and a graph autoencoder for consensus representation learning.
Also, a reliable target distribution generation measure and a triplet
self-supervision strategy, which facilitate cross-modality information
exploitation, are designed for network training. Extensive experiments on six
benchmark datasets have demonstrated that the proposed DFCN consistently
outperforms the state-of-the-art deep clustering methods.
