This study investigates the clustering performance of Variational Autoencoders
(VAE) and Generative Autoencoders (GAE) on a migraine dataset. Autoencoders
(AEs) are increasingly used in unsupervised learning for their ability to reduce
dimensionality and capture complex data representations. In this work, both
VAE and GAE models were trained under identical conditions on a migraine
dataset to evaluate their effectiveness in generating latent representations for
clustering
