Process discovery aims to automatically discover a process model to explain the behavior of event traces recorded in an event log during the execution of the activities of an underlying process. Several powerful process discovery algorithms are already formulated in process mining to identify regular control-flow structures in event logs and strike different trade-offs between the accuracy in capturing the behavior recorded in the event $\log$ and the complexity of the derived process model. This is commonly done under the assumption that log event traces are distributed according to the Pareto principle with a large portion of event traces held by a small fraction of top-frequent variants. However, the Pareto principle is not always satisfied in several real-life, complex processes. For example, the majority of log event traces produced in various healthcare or gaming processes is often spanned on a high number of top-frequent varianttraces. Various techniques (e.g. event filtering, trace extraction and trace abstraction) are already formulated in process mining to cope traditional process discovery algorithms also with event logs that do not conform the Pareto principle. Following this line of research, we explore the performance of a trace extraction method introduced to support the quest for Pareto-like event trace distribution during process discovery. The trace extraction is done resorting to a deep embedding representation of event traces, which sees traces at an abstraction level that removes noise and anomalous trace excerpt by enabling the discovery of simpler process models with higher accuracy. The deep embedding is used in combination with clustering. Experiments with several benchmark event logs show the effectiveness of the two proposed methods also compared to prior methods.