A study of types of silhouettes in women’s clothing

This research focuses on reviewing fashion trends from the past decade, with a particular emphasis on identifying unique silhouettes that deviate from traditional classifications. These new silhouettes often combine two existing shapes, straight and A-line, or feature unusual combinations like fitted and colourful styles. The study seeks to expand the classification of women’s fashion silhouettes by introducing new combined shapes that reflect modern trends and consumer preferences. By integrating traditional and contemporary designs with qualitative and quantitative analysis, the research enhances statistical reliability and supports designers and retailers in creating and presenting more stylish, tailored garments. The study enhances silhouette classification for dresses and skirts by identifying four distinct groups of dresses and three groups of skirts using Principal Component Analysis (PCA). Combined with an advanced system that integrates both qualitative and quantitative analyses, this approach provides deeper insights into consumer preferences and emerging trends. The use of aspect-based form factors offers an efficient method for classifying silhouettes and contributes to improving upon previous research in this area. The findings of this study have practical applications for fashion designers, enabling them to create garments that more effectively express consumer tastes and better showcase clothing and decorations. Additionally, retailers can utilize these insights to improve the arrangement of merchandise in stores, thereby personalizing the shopping experience and boosting sales.