  The goal of lifetime clustering is to develop an inductive model that maps
subjects into $K$ clusters according to their underlying (unobserved) lifetime
distribution. We introduce a neural-network based lifetime clustering model
that can find cluster assignments by directly maximizing the divergence between
the empirical lifetime distributions of the clusters. Accordingly, we define a
novel clustering loss function over the lifetime distributions (of entire
clusters) based on a tight upper bound of the two-sample Kuiper test p-value.
The resultant model is robust to the modeling issues associated with the
unobservability of termination signals, and does not assume proportional
hazards. Our results in real and synthetic datasets show significantly better
lifetime clusters (as evaluated by C-index, Brier Score, Logrank score and
adjusted Rand index) as compared to competing approaches.
