The truncated Euclidean Hypergraph Average Commute Time Distance-based Clustering Technique - Tran, Loc; Phan, Kim Anh; Nguyen, Hieu; Tran, Linh (Engineering, Technology & Applied Science Research, 2025)
Clustering is a fundamental problem in data science, with popular approaches including k-means and spectral clustering. However, while k-means is typically limited to tabular datasets and spectral clustering is mainly effective on network data, this work introduces two new methods, the Euclidean hypergraph average commute time distance-based clustering and its truncated variant (i.e., the truncated eigen decomposition technique), which can be applied to any dataset type. Experimental results indicate that both proposed techniques perform at least as well as the conventional Euclidean graph average commute time distance-based clustering and, in some cases, even achieve better results (as measured by the Davies-Bouldin score) because the hypergraph structure captures higher-order relationships among samples. Overall, the proposed methods significantly outperform traditional k-means and spectral clustering when clustering feature vectors.