Hypergraph Convolutional Neural Network-Based Clustering Technique

Abstract

This paper presents the novel hypergraph convolutional neural network-based clustering technique. This technique is employed to solve the clustering problem for the two datasets, which are 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 will be employed to transform both the incidence matrix and the feature matrix from the high dimensional space to the low dimensional space. Finally, the k-means clustering technique will be applied to the transformed matrix. Experiments show that the performance of the hypergraph convolutional neural network-based clustering technique is better than the performance of the other classical clustering techniques.