Information systems supporting business processes generate event data which provide the starting point for a range of process mining techniques.\u3cbr/\u3eLion's share of real-life processes are complex and ad-hoc, which creates problems for traditional process mining techniques, that cannot deal with such unstructured processes.\u3cbr/\u3eFinding mainstream and deviating cases in such data is problematic, since most cases are unique and therefore determining what is normal or exceptional may depend on many factors.\u3cbr/\u3eTrace clustering aims to group similar cases in order to find variations of the process and to gain novel insights into the process at hand.\u3cbr/\u3eHowever, few trace clustering techniques take the context of the process into account and focus on the control-flow perspective only.\u3cbr/\u3eOutlier detection techniques provide only a binary distinction between normal and exceptional behavior, or depend on a normative process model to be present.\u3cbr/\u3eAs a result, existing techniques are less suited for processes with a high degree of variability.\u3cbr/\u3eIn this paper, we introduce a novel trace clustering technique that is able to find process variants as well as deviating behavior based on a set of selected perspectives.\u3cbr/\u3eEvaluation on both artificial and real-life event data reveals that additional insights can consequently be achieved
