  A variety of works in the literature strive to uncover the factors associated
with survival behaviour. However, the computational tools to provide such
information are global models designed to predict if or when a (survival) event
will occur. When approaching the problem of explaining differences in survival
behaviour, those approaches rely on (assumptions of) predictive features
followed by risk stratification. In other words, they lack the ability to
discover new information on factors related to survival. In contrast, we
approach such a problem from the perspective of descriptive supervised pattern
mining to discover local patterns associated with different survival
behaviours. Hence, we introduce the EsmamDS algorithm: an Exceptional Model
Mining framework to provide straightforward characterisations of subgroups
presenting unusual survival models -- given by the Kaplan-Meier estimates. This
work builds on the Esmam algorithm to address the problem of pattern redundancy
and provide a more informative and diverse characterisation of survival
behaviour.
