Analyzing how patterns evolve over time in multi-dimensional datasets is challenging—traditional time-series methods often struggle with interpretability when comparing multiple entities across different scales. This talk introduces a clustering-based framework that transforms continuous data into categorical trajectories, enabling intuitive visualization and comparison of temporal patterns.What & Why: The method combines quartile-based categorization with modified Hamming distance to create interpretable "trajectory fingerprints" for entities over time. This approach is particularly valuable for policy analysis, economic comparisons, and any domain requiring longitudinal pattern recognition.Who: Data scientists and analysts working with temporal datasets, policy researchers, and anyone interested in comparative analysis across entities with different scales or distributions.Type: Technical presentation with practical implementation examples using Python (pandas, scikit-learn, matplotlib). Moderate mathematical content balanced with intuitive visualizations.Takeaway: Attendees will learn a novel approach to temporal pattern analysis that bridges the gap between complex statistical methods and accessible, policy-relevant insights. You'll see practical implementations analyzing 60+ years of fiscal policy data across 8 countries, with code available for adaptation to your own datasets.