Survival analysis is a branch of statistics for analyzing the expected duration of the time until the event of interest happens. It is not only applicable to biomedical problems but it can be widely used in almost every domain since there is a relevant data structure available. Recent studies have shown that it is a powerful approach for risk stratification. Since it is a well established statistical technique, there have been several studies that combine survival analysis with machine learning algorithms in order to obtain better performances. Additionally in the machine learning scientific field the usage of different data modalities has been proven to enhance the performance of predictive models. The majority of the scientific outcomes in the survival analysis domain have focused on modeling survival data and building robust predictive models for time to event estimation. Clustering based on risk-profiles is partly under-explored in machine learning, but is critical in applications domains such as clinical decision making. Clustering in terms of survivability is very useful when there is a need to identify unknown sub-populations in the overall data. Such techniques aim for identification of clusters whose lifetime distributions significantly differs, which is something that is not able to be done by applying traditional clustering techniques. In this survey we present research studies in the aforementioned domain with an emphasis on techniques for clustering censored data and identifying various risk level groups.

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