Traditional survival analysis estimates the instantaneous failure rate of an event and predicts survival probabilities distributions. In fact, in a set of censored data there may exist several sub-populations with various risk profiles or survival distributions, for which regular survival analysis approaches do not take into consideration. Consequently, there is a need for discovering such sub-populations with unambiguous risk profiles and survival distributions. In this work, we propose a modified version of the K-Medoids algorithm which can be used to efficiently cluster censored data and identify diverse groups with distinct lifetime distributions.