Volume 13, Issue 3
Knowledge Discovery and Data Mining-based Garment Size Selection for Mass Customization

Zhu-Jun Wang, Jian-Ping Wang, Xian-Yi ZengYing-Mei Xing

Journal of Fiber Bioengineering & Informatics, 13 (2020), pp. 113-128.

Published online: 2020-11

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  • Abstract

In this study, a new knowledge discovery and data mining-based technique has been proposed for garment size selection. It could be split into four sequential parts principally, involving data preparation, data preprocessing, fit models setting, and size selection. Two cases of mass customization, representing the top and bottom garments respectively, were utilized to expounding the implementation of the presented approach. After data preprocessing, key body dimensions were identified using the hierarchical clustering algorithm. Next, the enumeration algorithm was utilized by listing all the possible values while computing the distance between the target population and the fit models. Afterwards, an improved K-means clustering algorithm and support vector machine (SVM) method were utilized to size selection, respectively. Eventually, the SVM-based solution was considered as the optimal solution after being evaluated by the aggregate loss of fit, number of poor fit, accommodation rate of ideal fit, and number of sizes employed. The experimental results demonstrate that the present approach is a low-cost and high-effective improvement for size selection by exploiting the potentials of the existing sizing system, without creating new sizing systems. Moreover, the proposed approach can easily be applied to any type of garment in a flexible manner.

  • Keywords

Garment Size Selection Mass Customization Data Mining Knowledge Discovery Support Vector Machine.

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address

wangjp@dhu.edu.cn (Jian-Ping Wang)

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@Article{JFBI-13-113, author = {Wang , Zhu-Jun and Wang , Jian-Ping and Zeng , Xian-Yi and Xing , Ying-Mei}, title = {Knowledge Discovery and Data Mining-based Garment Size Selection for Mass Customization}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2020}, volume = {13}, number = {3}, pages = {113--128}, abstract = {

In this study, a new knowledge discovery and data mining-based technique has been proposed for garment size selection. It could be split into four sequential parts principally, involving data preparation, data preprocessing, fit models setting, and size selection. Two cases of mass customization, representing the top and bottom garments respectively, were utilized to expounding the implementation of the presented approach. After data preprocessing, key body dimensions were identified using the hierarchical clustering algorithm. Next, the enumeration algorithm was utilized by listing all the possible values while computing the distance between the target population and the fit models. Afterwards, an improved K-means clustering algorithm and support vector machine (SVM) method were utilized to size selection, respectively. Eventually, the SVM-based solution was considered as the optimal solution after being evaluated by the aggregate loss of fit, number of poor fit, accommodation rate of ideal fit, and number of sizes employed. The experimental results demonstrate that the present approach is a low-cost and high-effective improvement for size selection by exploiting the potentials of the existing sizing system, without creating new sizing systems. Moreover, the proposed approach can easily be applied to any type of garment in a flexible manner.

}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbim00354}, url = {http://global-sci.org/intro/article_detail/jfbi/18364.html} }
TY - JOUR T1 - Knowledge Discovery and Data Mining-based Garment Size Selection for Mass Customization AU - Wang , Zhu-Jun AU - Wang , Jian-Ping AU - Zeng , Xian-Yi AU - Xing , Ying-Mei JO - Journal of Fiber Bioengineering and Informatics VL - 3 SP - 113 EP - 128 PY - 2020 DA - 2020/11 SN - 13 DO - http://doi.org/10.3993/jfbim00354 UR - https://global-sci.org/intro/article_detail/jfbi/18364.html KW - Garment Size Selection KW - Mass Customization KW - Data Mining KW - Knowledge Discovery KW - Support Vector Machine. AB -

In this study, a new knowledge discovery and data mining-based technique has been proposed for garment size selection. It could be split into four sequential parts principally, involving data preparation, data preprocessing, fit models setting, and size selection. Two cases of mass customization, representing the top and bottom garments respectively, were utilized to expounding the implementation of the presented approach. After data preprocessing, key body dimensions were identified using the hierarchical clustering algorithm. Next, the enumeration algorithm was utilized by listing all the possible values while computing the distance between the target population and the fit models. Afterwards, an improved K-means clustering algorithm and support vector machine (SVM) method were utilized to size selection, respectively. Eventually, the SVM-based solution was considered as the optimal solution after being evaluated by the aggregate loss of fit, number of poor fit, accommodation rate of ideal fit, and number of sizes employed. The experimental results demonstrate that the present approach is a low-cost and high-effective improvement for size selection by exploiting the potentials of the existing sizing system, without creating new sizing systems. Moreover, the proposed approach can easily be applied to any type of garment in a flexible manner.

Zhu-Jun Wang, Jian-Ping Wang, Xian-Yi Zeng & Ying-Mei Xing. (2020). Knowledge Discovery and Data Mining-based Garment Size Selection for Mass Customization. Journal of Fiber Bioengineering and Informatics. 13 (3). 113-128. doi:10.3993/jfbim00354
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