TY - JOUR AU - Tu, Wenxuan AU - Zhou, Sihang AU - Liu, Xinwang AU - Guo, Xifeng AU - Cai, Zhiping AU - Zhu, En AU - Cheng, Jieren PY - 2021/05/18 Y2 - 2024/03/28 TI - Deep Fusion Clustering Network JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 11 SE - AAAI Technical Track on Machine Learning IV DO - 10.1609/aaai.v35i11.17198 UR - https://ojs.aaai.org/index.php/AAAI/article/view/17198 SP - 9978-9987 AB - 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. ER -