  Survival analysis aims to predict the timing of future events across various
fields, from medical outcomes to customer churn. However, the integration of
clustering into survival analysis, particularly for precision medicine, remains
underexplored. This study introduces SurvMixClust, a novel algorithm for
survival analysis that integrates clustering with survival function prediction
within a unified framework. SurvMixClust learns latent representations for
clustering while also predicting individual survival functions using a mixture
of non-parametric experts. Our evaluations on five public datasets show that
SurvMixClust creates balanced clusters with distinct survival curves,
outperforms clustering baselines, and competes with non-clustering survival
models in predictive accuracy, as measured by the time-dependent c-index and
log-rank metrics.
