Journal of Engineering, Project, and Production Management, 2026, 16(6), 2026-143
Data-Driven Color Palette Optimization for Interior Decoration: A Decision-Support Framework Based on Cluster Analysis and Emotional Needs Mapping
Lecture, Department of Civil Engineering, Changzhi Vocational and Technical College, Changzhi, 046011, China, E-mail: weiwang2369@163.com
Project Management
Received January 27, 2026; revised March 23, 2026; accepted September 18, 2026
Available online September 27, 2026
Abstract: The color selection in interior decoration design is based on the subjective aesthetic of the designer, but this can result in low design efficiency and difficulty in considering human emotional needs. To address these issues, this study proposes an objective design-assistance method based on K-means clustering in color space. The method first systematically collects real indoor scene image samples, then extracts high-dimensional features such as color matrices and color histograms from two color modes. Finally, the K-means algorithm automatically categorizes samples based on color characteristics and then correlates them with latent emotional needs. The results indicated that color space conversion can be achieved for different indoor scenes. In a one-way analysis of variance, the p-values for calm, exciting, cool and warm across different emotional needs are all less than 0.001. Therefore, the color space clustering results are applicable, and designs that consider emotional needs are practical. This method can automatically identify effective color designs from design cases, and its results can provide quantifiable color references for interior decoration with different emotional orientations.
Keywords: Interior decoration; cluster analysis; emotional needs, lab color; practical color coordinate system (PCCS).
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Citation: Wang, W. (2026). Data-Driven Color Palette Optimization for Interior Decoration: A Decision-Support Framework Based on Cluster Analysis and Emotional Needs Mapping. Journal of Engineering, Project, and Production Management, 16(6), 2026-143.
