  The goal of cluster analysis in survival data is to identify clusters that
are decidedly associated with the survival outcome. Previous research has
explored this problem primarily in the medical domain with relatively small
datasets, but the need for such a clustering methodology could arise in other
domains with large datasets, such as social networks. Concretely, we wish to
identify different survival classes in a social network by clustering the users
based on their lifespan in the network. In this paper, we propose a decision
tree based algorithm that uses a global normalization of $p$-values to identify
clusters with significantly different survival distributions. We evaluate the
clusters from our model with the help of a simple survival prediction task and
show that our model outperforms other competing methods.
