Data mining-based optimal assignment of apparel size for mass customization

In this study, we have explored and discussed the data mining-based solutions to apparel size assignment using approach principle, K-means clustering, and support vector machine, respectively. A case of mass customization for men's pants in China with 200 adult males were employed to validate a...

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Autores principales:	Zhujun Wang, Cheng Chi, Mengyun Zhang, Xianyi Zeng, Pascal Bruniaux, Jianping Wang, Yingmei Xing
Formato:	article
Lenguaje:	EN
Publicado:	TU Dresden; Faculty of Mechanical Science and Engineering;Chair of Development and Assembly of Textile Products 2020
Materias:	garment size mass customization data mining clustering algorithm k-means clustering support vector machine Textile bleaching, dyeing, printing, etc. TP890-933 Engineering machinery, tools, and implements TA213-215
Acceso en línea:	https://doaj.org/article/553eb18e256d4e0696ac959fae0bbbcb
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id	oai:doaj.org-article:553eb18e256d4e0696ac959fae0bbbcb
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spelling	oai:doaj.org-article:553eb18e256d4e0696ac959fae0bbbcb2021-11-08T17:12:34ZData mining-based optimal assignment of apparel size for mass customization2701-939X10.25367/cdatp.2020.1.p20-29https://doaj.org/article/553eb18e256d4e0696ac959fae0bbbcb2020-08-01T00:00:00Zhttps://journals.qucosa.de/cdatp/article/view/4https://doaj.org/toc/2701-939XIn this study, we have explored and discussed the data mining-based solutions to apparel size assignment using approach principle, K-means clustering, and support vector machine, respectively. A case of mass customization for men's pants in China with 200 adult males were employed to validate and evaluate the solutions. After anthropometric data acquisition and preprocessing, three key body dimensions were identified based on hierarchical clustering, as well as their ranges and fit models. Sequentially, we calculated all the possible values of the distance between the target population and the fit models by the enumeration algorithm. Afterward, we assigned the garment sizes for the target population using the abovementioned data mining approaches. Lastly, the solution based on support machine was considered as the optimal solution for pants mass customization after being comprehensively assessed by the aggregate loss of fit, the number of poor fit, accommodation rate of ideal fit, and the number of garment size employed, since it employed only 48 sizes to reach the accommodation rate of the target population up to 82%. The experimental results demonstrate that the present solution is a low-cost method for size assignment by exploiting the potentials of the existing sizing system, instead of creating new sizing systems, and also easy to be flexibly extended to any types of garments.Zhujun WangCheng ChiMengyun ZhangXianyi ZengPascal BruniauxJianping WangYingmei XingTU Dresden; Faculty of Mechanical Science and Engineering;Chair of Development and Assembly of Textile Productsarticlegarment sizemass customizationdata miningclustering algorithmk-means clusteringsupport vector machineTextile bleaching, dyeing, printing, etc.TP890-933Engineering machinery, tools, and implementsTA213-215ENCommunications in Development and Assembling of Textile Products, Vol 1, Iss 1, Pp 20-29 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	garment size mass customization data mining clustering algorithm k-means clustering support vector machine Textile bleaching, dyeing, printing, etc. TP890-933 Engineering machinery, tools, and implements TA213-215
spellingShingle	garment size mass customization data mining clustering algorithm k-means clustering support vector machine Textile bleaching, dyeing, printing, etc. TP890-933 Engineering machinery, tools, and implements TA213-215 Zhujun Wang Cheng Chi Mengyun Zhang Xianyi Zeng Pascal Bruniaux Jianping Wang Yingmei Xing Data mining-based optimal assignment of apparel size for mass customization
description	In this study, we have explored and discussed the data mining-based solutions to apparel size assignment using approach principle, K-means clustering, and support vector machine, respectively. A case of mass customization for men's pants in China with 200 adult males were employed to validate and evaluate the solutions. After anthropometric data acquisition and preprocessing, three key body dimensions were identified based on hierarchical clustering, as well as their ranges and fit models. Sequentially, we calculated all the possible values of the distance between the target population and the fit models by the enumeration algorithm. Afterward, we assigned the garment sizes for the target population using the abovementioned data mining approaches. Lastly, the solution based on support machine was considered as the optimal solution for pants mass customization after being comprehensively assessed by the aggregate loss of fit, the number of poor fit, accommodation rate of ideal fit, and the number of garment size employed, since it employed only 48 sizes to reach the accommodation rate of the target population up to 82%. The experimental results demonstrate that the present solution is a low-cost method for size assignment by exploiting the potentials of the existing sizing system, instead of creating new sizing systems, and also easy to be flexibly extended to any types of garments.
format	article
author	Zhujun Wang Cheng Chi Mengyun Zhang Xianyi Zeng Pascal Bruniaux Jianping Wang Yingmei Xing
author_facet	Zhujun Wang Cheng Chi Mengyun Zhang Xianyi Zeng Pascal Bruniaux Jianping Wang Yingmei Xing
author_sort	Zhujun Wang
title	Data mining-based optimal assignment of apparel size for mass customization
title_short	Data mining-based optimal assignment of apparel size for mass customization
title_full	Data mining-based optimal assignment of apparel size for mass customization
title_fullStr	Data mining-based optimal assignment of apparel size for mass customization
title_full_unstemmed	Data mining-based optimal assignment of apparel size for mass customization
title_sort	data mining-based optimal assignment of apparel size for mass customization
publisher	TU Dresden; Faculty of Mechanical Science and Engineering;Chair of Development and Assembly of Textile Products
publishDate	2020
url	https://doaj.org/article/553eb18e256d4e0696ac959fae0bbbcb
work_keys_str_mv	AT zhujunwang dataminingbasedoptimalassignmentofapparelsizeformasscustomization AT chengchi dataminingbasedoptimalassignmentofapparelsizeformasscustomization AT mengyunzhang dataminingbasedoptimalassignmentofapparelsizeformasscustomization AT xianyizeng dataminingbasedoptimalassignmentofapparelsizeformasscustomization AT pascalbruniaux dataminingbasedoptimalassignmentofapparelsizeformasscustomization AT jianpingwang dataminingbasedoptimalassignmentofapparelsizeformasscustomization AT yingmeixing dataminingbasedoptimalassignmentofapparelsizeformasscustomization
_version_	1718441473235484672

Data mining-based optimal assignment of apparel size for mass customization

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