Purpose – The purpose of this paper is to develop a new suit sizing system based on up‐dated data, using data mining techniques, to improve the final quality and reduce the waste of fabric. This paper aims to investigate the effect of data reduction on the final fitness of the sizing chart. Design/methodology/approach – Principal component analysis is applied to reduce the sizing variables, non‐hierarchical clustering approach is used to segment the heterogeneous population to more homogeneous one, and the aggregate loss of fitness is used to evaluate the resulted sizing chart. Findings – The results show that, when principal component analysis reduces the ten sizing variables to two main components, the final fitness for the resulted sizing chart is the best. These two main components are height and circumference. The hierarchical clustering approach could effectively group all body type to seven clusters. The resulted sizing chart could be used as a reference for suit manufacturers. Practical implications – Due to wide differences in race, nutrition and climate, people who live in different countries have their own body size; also, most of current sizing systems are out‐dated, so there is an urgent need to develop a new sizing system. Due to the growing rate of globalization, the final results will be useful for those companies wanting to connect to global business chains. Originality/value – This work introduces the first suit sizing systems, based on data mining, for Iranian males, that has more fitness in comparison to the current sizing chart. The effect of the number of principal components on the final fitness of a sizing system is introduced as an innovative way, to avoid losing useful data during data reduction process with principal component analysis.
International Journal of Clothing Science and Technology – Emerald Publishing
Published: Feb 24, 2012
Keywords: Clothing; Data mining; Data analysis; Sizing system; Data optimization; Principal component analysis; Clustering
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