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A variety of works in the literature strive to uncover the factors associatedwith survival behaviour. However, the computational tools to provide suchinformation are global models designed to predict if or when a (survival) eventwill occur. When approaching the problem of explaining differences in survivalbehaviour, those approaches rely on (assumptions of) predictive featuresfollowed by risk stratification. In other words, they lack the ability todiscover new information on factors related to survival. In contrast, weapproach such a problem from the perspective of descriptive supervised patternmining to discover local patterns associated with different survivalbehaviours. Hence, we introduce the EsmamDS algorithm: an Exceptional ModelMining framework to provide straightforward characterisations of subgroupspresenting unusual survival models -- given by the Kaplan-Meier estimates. Thiswork builds on the Esmam algorithm to address the problem of pattern redundancyand provide a more informative and diverse characterisation of survivalbehaviour.

EsmamDS: A more diverse exceptional survival model mining approach