Deep Fusion Clustering Network

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. Our code is publicly available at https: //github. com/WxTu/DFCN.

Authors

• Wenxuan Tu School of Computer Science and Technology, Hainan University, Haikou, China Hainan Blockchain Technology Engineering Research Center, Haikou, China
• Sihang Zhou College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
• Xinwang Liu National University of Defense Technology
• Xifeng Guo
• Zhiping Cai National University of Defense Technology
• En Zhu National University of Defense Technology
• Jieren Cheng School of Computer Science and Technology, Hainan University, Haikou, China Hainan Blockchain Technology Engineering Research Center, Haikou, China

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