Application of Fuzzy Clustering Methodology for Garment Sizing

Abstract

With the growing demand for Ready-To-Wear outfits especially in African textile prints, the currently used European, American and Asian garment sizing systems seems unsuitable for the Nigerian garment industry where customer's choose clothing item not only due to fit in terms of body measurements but also the dress culture, style, preference and some other implicit requirements. This study aims to develop a size chart for different styles of trousers worn by Nigeria male population. Anthropometric data of 500 customers were taken in a natural random process and from stable tailoring establishments. The data was analysed using descriptive statistics and the fuzzy clustering methodology (FCM) was used as a suggestive approach which describes subjectivity in customer preferences. Analysis of the FCM output shows that the number of individual measurements with misfit has no significant difference (F estimated = 1.119, p-value=0.375 and F critical = 2.866) across cluster. The percentages of misfit were 38.0, 23.4, 31.6, 31.4 and 3.8% for hip measurement, length, waist, thigh and bottomgirth respectively. The developed sizing system which reflects subjectivity in customer's selection of trouser may also enhance both producer and retailer's production and replenishment policy.