In this paper, we propose a unified framework to enhance the explainability of clustering results by bridging methodologies from Trace Clustering in Process Mining and Sequence Pattern Mining. Our approach formalizes a common representation that transforms event logs and trajectories into structured sequences, ensuring consistency across sequential data types. We first introduce a systematic process for converting raw data into structured sequences, allowing for a coherent analysis of logs and trajectories. We then explore various clustering techniques, including Trace Clustering in Process Mining, Sequential Pattern Mining, and the GALACTIC approach, emphasizing their role in uncovering interpretable behavioral patterns. Through real-world dataset examples, on which we conducted behavioral pattern extraction studies, we demonstrate how integrating these methodologies enables a more comprehensive understanding of sequential data. Furthermore, we construct an event log and derive a process model from clusters obtained via Sequence Pattern Mining, illustrating how specific patterns contribute to cluster emergence. This structured approach enhances interpretability and provides a foundation for future research on explainable clustering.