The use of dynamic event trees within probabilistic safety analyses provides insights in the effects of uncertainties on time-dependent processes for complex systems. The approach thereby overcomes the limitations of a classical probabilistic safety analysis (PSA) with a predefined fixed order of events. The effects of the high-dimensional parameter space induced by state and time variations of events can be simulated and represented using a Monte Carlo approach in combination with the dynamic event tree simulation. This however leads to large samples of event trees and time-dependent scenarios requiring machine learning algorithms to analyze the amount of data produced. The first step of such a data analysis is a selection of relevant features, e.g. time sequences of parameters, in order to reduce the dimensionality and the redundancy of information. In a second step, an unsupervised classification is applied to group the different scenarios in several clusters, which can then be further analyzed. Parameterizing these clusters can provide further insights in the influence of uncertainties on the PSA results. The software tool MCDET (Monte Carlo Dynamic Event Tree) has recently been restructured and modularized to a tool based on python with further enhancements including generic feature selection and cluster identification algorithms for the post-processing. In this contribution, recent developments of MCDET including the software restructuring and the data analyzing tools are presented. In addition, case studies for a simplified tank overflow and a steam generator tube rupture scenario are shown.