American Journal of Data Mining and Knowledge Discovery

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Application of Fuzzy Clustering Methodology for Garment Sizing

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 (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.

DOI 10.11648/j.ajdmkd.20190401.15
Published in American Journal of Data Mining and Knowledge Discovery (Volume 4, Issue 1, June 2019)
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), 2024. Published by Science Publishing Group

Keywords

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

References
[1] Izzah N, Rahman A, Dawal SZ, Yusoff N, Sofia N, Kamil M. Anthropometric measurements among four Asian countries in designing sitting and standing workstations. Sādhanā [Internet]. 2018; 43 (1): 1–9. Available from: https://doi.org/10.1007/s12046-017-0768-8
[2] Otieno, A. D., Mehtre, A., Fera O, Lema O., O., Gebeyehu S. Developing Standard Size Charts for Ethiopian Men between the Ages of 18-26 through Anthropometric Survey. J Text Apparel, Technol Manag. 2016; 10 (1): 1–10.
[3] Hsu C, Lee T, Kuo H. Mining the Body Features to Develop Sizing Systems to Improve Business Logistics and Marketing Using Fuzzy Clustering Data Mining. WSEAS Trans Comput. 2009; 8 (7): 1215–24.
[4] Adelaja O, Salusso CJ. Designing apparel for Nigerian women : addressing visual appeal, body type and sizing. In: International Textile and Apparel Association Annual Conference Proceedings. 2015. p. 1–3.
[5] Salusso. A method for classifying adult female body form variation in relation to the US Standard for apparel sizing. Doctoral Dissertation, University of Minnesota; 1982.
[6] Tryfos P. An integer programming approach to the apparel sizing problem. J Oper Res Soc. 1986; 37 (10): 1001-6.
[7] McCulloch, C. E., Paal, B. and Ashdown SP. An optimization approach to apparel sizing. J Oper Res Soc. 1998; 49 (5): 492-9.
[8] Gupta, D. and Gangadhar P. A statistical model for developing body size charts for garments. Int J Cloth Sci Technol. 2004; 16: 458–69.
[9] Gupta, Deepti, N. Garg KA and NP. Developing Body Measurement Charts for Garment Manufacture Based on a Linear Programming Approach. J Text Apparel, Technol Manag. 2006; 5 (1): 1–13.
[10] S. Z. Loker, S. Ashdown KS. Size-specific analysis of body scan data to improve apparel fit, J. of Textile and Apparel. J Text Apparel, Technol Manag. 2005; 4 (3): 1–13.
[11] Lee Y-S. Standards Sizing for Clothing based on Anthropometry Data. J Ergon Soc Korea. 2014; 33 (5): 337–54.
[12] Beshah B, Belay B, Tilahun S, Tizazu B, Matebu A. Anthropometric data of Bahirdar City ’ s adult men for clothing design. Int J Vocat Tech Educ. 2014; 6 (5): 51–7.
[13] J SS and, Pandya S. An Overview of Partitioning Algorithms in Clustering Techniques. Int J Adv Res Comput Eng Technol. 2016; 5 (6): 1943–6.
[14] Zakaria, N., Mohd, J. S., Taib, N., Tan, Y. Y. and Wah YB. Using data mining technique to explore anthropometric data towards the development of sizing system. Int Symp Inform Technol. 2008; 2: 1–7.
[15] Bagherzadeh, R., Latifi, M. and Faramarzi AR. Employing a three-stage data mining procedure to develop sizing system. WASJ. 2010; 8: 923–929.
[16] Elfaki EF, Ali AHM. A Comparison Between The New Established Sizing Systems Sud And Sur Military Clothing Factory Sizing Chart For Poshirt (U4)-Part 1. Int J Adv Res Eng Appl Sci. 2016; 5 (1): 32–41.
[17] M. Vishnu Vardhana Rao, Sharad Kumar and G. N. V. Brahmam. A study of the geographical clustering of districts in Uttar Pradesh using nutritional anthropometric data of preschool children. Indian J Med Res. 2013; 137 (1): 73–81.
[18] Majumder J, Sharma LK. Identifying Body Size Group Clusters from Anthropometric Body Composition Indicators. J Ecophysiol Occup Hlth. 2015; 15: 81–8.
[19] Gupta, Deepti, Garg, Naveen, Arora Komal PN. Developing body measurement charts for Garment Manufacture Based on a Linear Programming Approach. J Text Apparel, Technol Manag. 2006; 5 (1): 1–13.
[20] Rasheed A, Zeng X, Thomassey S. An Approach to the Design of a Fuzzy Logic Model for the Ease Allowance Calculation in Loose Fitting Knee Length Ladies Trousers. J Eng Fiber Fabr. 2013; 8 (4): 4–9.
[21] Cui L, Zhang Y, Deng J, Xu M. A novel multi-item joint replenishment problem considering multiple type discounts. PLoS One [Internet]. 2018; 13 (6): 1–19. Available from: http://dx.doi.org/10.1371/journal.pone.0194738.
[22] Bezdek J. Pattern Recognition with Fuzzy Objective Function, New York. Plenum,. 1981.
[23] Ghosh S. Comparative Analysis of K-Means and Fuzzy C- Means Algorithms. 2013; 4 (4): 35–9.
[24] Hogo MA. Evaluation of E-Learners Behaviour using Different Fuzzy Clustering Models : A Comparative Study. Int J Comput Sci Inf Secur. 2010; 7 (2): 131–40.
[25] Kolawole A. Investigation of The Relationship between Fit and Garment Sizing Parameters. University of Ibadan, Nigeria; 2016.
[26] Dallal G Wilkinson L. An Analytic Approximation to the Distribution of Lilliefors’s Test Statistic for Normality. Am Stat. 1986; 40 (4): 294–6.
[27] Hernández N, Mattila H, Berglin L. A systematic model for improving theoretical garment fit. J Fash Mark Manag An Int J. 2018; 22 (4): 527–39.
Author Information
  • Department of Industrial and Production Engineering, University of Ibadan, Ibadan, Nigeria

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

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

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    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://download.sciencepg.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
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    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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