Analyzing the Application of Minimalism in Product Appearance Design using Associative Data Mining Optimized Feature Selection and Deep Learning of Bang&Olufsen Products

Abstract

The application of minimalism in product appearance design has gained significant attention in recent years due to its focus on simplicity, functionality, and aesthetic appeal. This paper explores the use of Associative Data Mining Optimized Feature Selection (ADM-OFS) classifier with deep learning techniques to analyze the application of minimalism in product appearance design, using Bang&Olufsen products as a case study. The proposed ADM-OFS perform feature selection is performed using an associative data mining approach, which estimates the most relevant and influential features that contribute to minimalistic design. The optimized feature selection process enhances the accuracy and efficiency of the analysis by reducing the dimensionality of the dataset while retaining its essential characteristics. The ADM-OFS model comprises the deep learning techniques employed to capture intricate patterns and relationships between minimalism and product appearance design. The deep learning model is trained on the dataset, enabling it to recognize complex visual features and make predictions about the minimalistic qualities of new product designs. The findings of ADM-OFS provide valuable insights into the application of minimalism in product appearance design, specifically in the context of Bang&Olufsen products. The analysis demonstrated the ADM-OFS classifier with deep learning, in analyzing and interpreting the application of minimalism in product appearance design. The findings of ADM-OFS stated that the designers, manufacturers, and researchers in their pursuit of creating visually appealing and functionally efficient products that embody the principles of minimalism.