Looking for representative fit models for apparel sizing

• Identification of optimal fit models to improve size fitting
• Outlier detection to optimize the companies' market share
• Hierarchical partitioning around medoids (HIPAM) clustering algorithm applied to anthropometric data
• Apparel sizing

This paper is concerned with the generation of optimal fit models for use in apparel design. Representative fit models or prototypes are important for defining a meaningful sizing system. However, there is no agreement among apparel manufacturers and each one has their own prototypes and size charts i.e. there is a lack of standard sizes in garments from different apparel manufacturers.We propose two algorithms based on a new hierarchical partitioning around medoids clustering method originally developed for gene expression data. We are concerned with a different application; therefore, the dissimilarity between the objects has to be different and must be designed to deal with anthropometric features. Furthermore, one of the algorithms incorporates a different rule to split the clusters, which, in our case, provides better results. Our procedures not only make it possible to obtain optimal prototypes, but also to detect outliers. These outliers should be removed before defining prototypes so that the companies' market share can be optimized.All the analyses are performed using the anthropometric database obtained from a survey of the Spanish female population.