Evaluation of Deep Clustering Methods on High Dimensional Tabular Biomedical Data

Ruben E. Munoz-Cabrera, Manuel Campos, Jose M. Juarez

Abstract: Clustering is a fundamental task in biomedical research, particularly for enabling stratification of patients. Traditional clustering techniques often struggle in this domain due to the vast number of attributes to be considered and the presence of complex, non-linear data structures. Deep Clustering (DC) methods, which combine neural networks with classical clustering techniques, have emerged as a promising alternative to address this challenge. To this day, most DC research has focused on images or text, however for many biomedical problems data is in a tabular format.

External IDs:doi:10.1007/978-3-032-10661-2_34