Process mining often yields highly complex “Spaghetti Models”, making them difficult to interpret and impeding informed decision-making. Therefore, researchers have explored clustering of event logs to simplify process models and reduce their complexity. However, the unsupervised nature of clustering can introduce an interpretation gap, necessitating manual effort to identify differences and similarities across the resulting process models. To address these issues, we propose an explainable clustering approach that identifies key subprocesses and applies eXplainable Artificial Intelligence (XAI) techniques to clarify the rationale behind model partitioning. Moreover, we integrate a Large Language Model (LLM) into the process discovery procedure to generate natural language descriptions and compare the discovered process models, enhancing user understanding, engagement, and making complex technical details more accessible. A case study demonstrates that our method operates effectively across various LLMs, preserving vital contextual information while simplifying the process discovery workflow. Our findings reveal that larger models generally ensure completeness, whereas smaller ones offer more efficiency at the expense of explanation quality, highlighting the importance of a balanced LLM choice for practical applications.