Application of Fuzzy Clustering Methodology for Garment Sizing

Adepeju Abimbola Opaleye; Adekunle Kolawole; Oliver Ekepre Charles-Owaba

doi:doi:10.11648/j.ajdmkd.20190401.15

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Application of Fuzzy Clustering Methodology for Garment Sizing

Adepeju Abimbola Opaleye, Adekunle Kolawole, Oliver Ekepre Charles-Owaba

Published in American Journal of Data Mining and Knowledge Discovery (Volume 4, Issue 1)

Received: 16 April 2019 Accepted: 28 May 2019 Published: 12 June 2019

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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 bottom-girth 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.

Published in	American Journal of Data Mining and Knowledge Discovery (Volume 4, Issue 1)
DOI	10.11648/j.ajdmkd.20190401.15
Page(s)	24-31
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Ready-To-Wear, Size Chart, Trousers, Fuzzy Clustering

References

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APA Style

Adepeju Abimbola Opaleye, Adekunle Kolawole, Oliver Ekepre Charles-Owaba. (2019). Application of Fuzzy Clustering Methodology for Garment Sizing. American Journal of Data Mining and Knowledge Discovery, 4(1), 24-31. https://doi.org/10.11648/j.ajdmkd.20190401.15

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ACS Style

Adepeju Abimbola Opaleye; Adekunle Kolawole; Oliver Ekepre Charles-Owaba. Application of Fuzzy Clustering Methodology for Garment Sizing. Am. J. Data Min. Knowl. Discov. 2019, 4(1), 24-31. doi: 10.11648/j.ajdmkd.20190401.15

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AMA Style

Adepeju Abimbola Opaleye, Adekunle Kolawole, Oliver Ekepre Charles-Owaba. Application of Fuzzy Clustering Methodology for Garment Sizing. Am J Data Min Knowl Discov. 2019;4(1):24-31. doi: 10.11648/j.ajdmkd.20190401.15

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@article{10.11648/j.ajdmkd.20190401.15,
  author = {Adepeju Abimbola Opaleye and Adekunle Kolawole and Oliver Ekepre Charles-Owaba},
  title = {Application of Fuzzy Clustering Methodology for Garment Sizing},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {4},
  number = {1},
  pages = {24-31},
  doi = {10.11648/j.ajdmkd.20190401.15},
  url = {https://doi.org/10.11648/j.ajdmkd.20190401.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20190401.15},
  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 (Festimated= 1.119, p-value=0.375 and Fcritical= 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 bottom-girth 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.},
 year = {2019}
}

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TY  - JOUR
T1  - Application of Fuzzy Clustering Methodology for Garment Sizing
AU  - Adepeju Abimbola Opaleye
AU  - Adekunle Kolawole
AU  - Oliver Ekepre Charles-Owaba
Y1  - 2019/06/12
PY  - 2019
N1  - https://doi.org/10.11648/j.ajdmkd.20190401.15
DO  - 10.11648/j.ajdmkd.20190401.15
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 24
EP  - 31
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20190401.15
AB  - 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 (Festimated= 1.119, p-value=0.375 and Fcritical= 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 bottom-girth 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.
VL  - 4
IS  - 1
ER  -

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Author Information

Adepeju Abimbola Opaleye

Department of Industrial and Production Engineering, University of Ibadan, Ibadan, Nigeria
Adekunle Kolawole

Department of Industrial and Production Engineering, University of Ibadan, Ibadan, Nigeria
Oliver Ekepre Charles-Owaba

Department of Industrial and Production Engineering, University of Ibadan, Ibadan, Nigeria

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APA Style

Adepeju Abimbola Opaleye, Adekunle Kolawole, Oliver Ekepre Charles-Owaba. (2019). Application of Fuzzy Clustering Methodology for Garment Sizing. American Journal of Data Mining and Knowledge Discovery, 4(1), 24-31. https://doi.org/10.11648/j.ajdmkd.20190401.15

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ACS Style

Adepeju Abimbola Opaleye; Adekunle Kolawole; Oliver Ekepre Charles-Owaba. Application of Fuzzy Clustering Methodology for Garment Sizing. Am. J. Data Min. Knowl. Discov. 2019, 4(1), 24-31. doi: 10.11648/j.ajdmkd.20190401.15

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AMA Style

Adepeju Abimbola Opaleye, Adekunle Kolawole, Oliver Ekepre Charles-Owaba. Application of Fuzzy Clustering Methodology for Garment Sizing. Am J Data Min Knowl Discov. 2019;4(1):24-31. doi: 10.11648/j.ajdmkd.20190401.15

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@article{10.11648/j.ajdmkd.20190401.15,
  author = {Adepeju Abimbola Opaleye and Adekunle Kolawole and Oliver Ekepre Charles-Owaba},
  title = {Application of Fuzzy Clustering Methodology for Garment Sizing},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {4},
  number = {1},
  pages = {24-31},
  doi = {10.11648/j.ajdmkd.20190401.15},
  url = {https://doi.org/10.11648/j.ajdmkd.20190401.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20190401.15},
  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 (Festimated= 1.119, p-value=0.375 and Fcritical= 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 bottom-girth 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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Application of Fuzzy Clustering Methodology for Garment Sizing
AU  - Adepeju Abimbola Opaleye
AU  - Adekunle Kolawole
AU  - Oliver Ekepre Charles-Owaba
Y1  - 2019/06/12
PY  - 2019
N1  - https://doi.org/10.11648/j.ajdmkd.20190401.15
DO  - 10.11648/j.ajdmkd.20190401.15
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 24
EP  - 31
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20190401.15
AB  - 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 (Festimated= 1.119, p-value=0.375 and Fcritical= 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 bottom-girth 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.
VL  - 4
IS  - 1
ER  -

Copy | Download