  This paper constitutes the novel hypergraph convolutional neural networkbased
clustering technique. This technique is employed to solve the clustering
problem for the Citeseer dataset and the Cora dataset. Each dataset contains
the feature matrix and the incidence matrix of the hypergraph (i.e.,
constructed from the feature matrix). This novel clustering method utilizes
both matrices. Initially, the hypergraph auto-encoders are employed to
transform both the incidence matrix and the feature matrix from high
dimensional space to low dimensional space. In the end, we apply the k-means
clustering technique to the transformed matrix. The hypergraph convolutional
neural network (CNN)-based clustering technique presented a better result on
performance during experiments than those of the other classical clustering
techniques.
