الخلاصة:
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. The models' latent spaces were clustered using K-means, and
performance was assessed using the Silhouette Score. Results indicate that the
GAE outperformed the VAE, achieving a Silhouette Score of 0.50 compared to
0.39 for the VAE, demonstrating the GAE's superior capacity to produce
meaningful latent representations. These findings underscore the importance of
selecting an appropriate autoencoder architecture for healthcare applications
involving clustering.
