LAUSR

Abstract This study aimed to establish a head anthropometric database for the Taiwanese population and to compare two clustering analysis results for separating head-shape clusters as well as generating sizing systems. Two-stage clustering and two-level self-organizing map (SOM) methods were both applied. The PCA results indicated four components that can explain over 85% of the variability. The two-stage clustering grouped the head anthropometric data into four head shape types, in which twelve sizes were extracted. The two-level SOM method identified three head shape types, in which nine sizes were extracted. The coverage of both methods reached over 80%. Comparing the results, the two-level SOM method is superior to the two-stage clustering method. The findings of this study demonstrated that the two-level SOM method is an effective analysis tool with the ability to visualize latent spatial structures and characteristics within large datasets. These results provide useful information for designing and manufacturing head-related products.