Analyzing Fashion Trends Using Hierarchical Clustering and Temporal Analysis

The fashion industry constantly evolves where art, culture, and commerce intersect, shaping how people express themselves through clothing. As Blair Waldorf famously stated, Fashion is the most powerful art. It’s movement, design, and architecture all in one. It shows the world who we are and what we want to be. This sentiment captures fashion’s role as both a creative and cultural force. Given its dynamic nature, analyzing fashion trends over time provides valuable insights into how styles emerge, evolve, and fade. Data from Vogue Runway was collected to explore these trends, covering Spring and Fall collectionsfrom New York and Paris between 1988 and 2024. Hierarchical clustering was used to categorize fashion trends, identifying distinct style patterns across the years. Next, time series analysis was applied to track the evolution of Ready-to-Wear and Couture collections. By integrating fashion analysis with machine learning techniques, this study highlights how data-driven approaches can enhance the understanding of fashion’s ever-changing landscape while preserving its creative essence.