2D nanopores can be used to electrophoretically drive DNA molecules, which can in turn be identified through measurable electronic current blockades. In this work, we use experimental data from molybdenum disulfide nanopores threading DNA nucleotides and propose a methodological approach to interpret DNA events. Specifically, the experimental ionic traces are used to train an unsupervised machine learning model for identifying distinct molecular events through the 2D nanopore. For the first time, we propose a clustering of experimental 2D nanopore data based on the ionic current blockade height and unrelated to the traditional dwell time for each DNA event. Within this approach, the blockade level information is implicitly included in the feature space analysis and does not need to be treated explicitly. We could show the higher efficiency of the blockade height over the traditional dwell time also in coping with sparse nanopore data sets. Our approach allows for a deep insight into characteristic molecular features in 2D nanopores and provides a feedback mechanism to tune these materials and interpret the measured signals. It has, thus, a high impact on the efficiency of 2D nanopore-based DNA sequencers.