Apparel sizing using trimmed PAM and OWA operators

Abstract

This paper is concerned with apparel sizing system design. One of the most important issues in the apparel development process is to define a sizing system that provides a good fit to the majority of the population. A sizing system classifies a specific population into homogeneous subgroups based on some key body dimensions. Standard sizing systems range linearly from very small to very large. However, anthropometric measures do not grow linearly with size, so they can not accommodate all body types. It is important to determine each class in the sizing system based on a real prototype that is as representative as possible of each class. In this paper we propose a methodology to develop an efficient apparel sizing system based on clustering techniques jointly with OWA operators. Our approach is a natural extension and improvement of the methodology proposed by McCulloch et al in 1998 [22],and we apply it to the anthropometric database obtained from a anthropometric survey of the Spanish female population, performed during 2006.

Anthropometry: An R Package for Analysis of Anthropometric Data

“…Next, the mathematical details behind this procedure are briefly explained. A detailed exposition is given in Ibáñez et al (2012b);Vinué (2014). The dissimilarity measure is defined as follows.…”

“…Second, the dissimilarity measure defined in McCulloch et al (1998) is modified using an OWA (ordered weighted average) operator to consider the user morphology. This approach was published in Ibáñez et al (2012b) and it is implemented in the trimowa function. Next, the mathematical details behind this procedure are briefly explained.…”

“…A similar code was used to obtain the results described in Ibáñez et al (2012b R> dataTrimowa <-sampleSpanishSurvey R> numVar <-dim(dataTrimowa) [2] R> bust <-dataTrimowa$bust R> bustSizes <-bustSizesStandard(seq (74, 102, 4), seq(107, 131, 6))…”

“…The more random repetitions, the more accurate the prototypes and the more representative of the size group. In Ibáñez et al (2012b), the number of random initializations was 600.…”

“…Four of them are aimed at segmenting the population into optimal size groups and obtaining size prototypes. The first one, hereafter referred to as trimowa, has been published in Ibáñez et al (2012b). It is based on using a special distance function that mathematically captures the idea of garment fit.…”