DASCDC: Dual Attention-based Sparse Common Deep Clustering Method

Yao Dong, Jin Shi, Yahan Deng, Yongfeng Dong, Ziqiu Wang

Abstract: Graph convolutional network (GCN) has been successfully applied in deep clustering on the high dimensional dataset, which is usually processed by extracting the feature and structure information. However, it lacks an effective fusion mechanism of common information between the feature and structure information and does not perform well on the sparse dataset. Therefore, we propose a dual attention-based sparse common deep clustering method(DASCDC). Firstly, we advance a sparse feature representation method with an adversarial loss function to solve the data sparsity problem and improve the discriminator’s performance. Then, an information interaction operator is designed to deeply mine the common information and enhance the information fusion capability. Finally, considering the different contribution degrees of various neighbour nodes and information, we promote a dual self-attention mechanism from the node and model levels. Experiment results indicate that the proposed DASCDC algorithm overperforms other state-of-art deep clustering algorithms on non-graph and graph datasets with high dimension and sparsity.