Indian Journal of Fibre & Textile Research

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Classification of Ring-Spun Yarns Using Cluster Analysis


Affiliations
1 Department of Textile Engineering, Faculty of Engineering, University of Bonab, Bonab 5551395133, Iran, Islamic Republic of
2 Biostatistics Unit, Department of Public Health, University of Liege, Belgium
 

The aim of this study is to classify ring-spun yarns according to their unevenness, imperfections, and hairiness parameters using cluster analysis. The mentioned features of ring-spun yarns are measured for five different ranks. Five ranks of ring-spun yarns including compact and conventional as well as combed and carded types are chosen and produced. In the modeling section, the model-based clustering method was applied as a strong method based on the distribution of each variable. In order to select the best fit and to find out the final clustering, bayesian information criterion (BIC) is applied. According to the results of modeling, five ranks of selected ring-spun yarns are classified in four clusters and the acceptable agreement is measured according to Cohen’s kappa method. The highest value for Kappa represents a high agreement to match between the clustering result and the real rank.

Keywords

Clustering Validation, Model-Based Clustering, Ring-Spun Yarn, Statistical Analysis.
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  • Rong GH, Slater K & Fei RC, J Text Inst, 85 (1992) 389.

  • Rwawiire S, Kasedde A, Nibikora I & Wandera G, JTATM, 8 (2014) 1.

  • Nikolic M, Stjepanovic Z, Lesjak F & Stritof A, Fibres Text East Eur, 11 (2003) 30.

  • Cyniak D, Czekalski J, Jackowski T & Popin L, Fibres Text East Eur, 14 (2006) 33.

  • Canoglu S & Yukseloglu SM, Fibres Text East Eur, 16 (2008) 34.

  • Teklehaymanot FK, Muma M & Zoubir AM, IEEE Trans. Signal Processing, 66 (2018) 5392.

  • Dahiya S, Nanda H, Artwani J & Varshney J, Int J Adv Trends Comput Sci Eng Inf technol, 9 (2020) 2138.

  • Akogul S & Erisoglu M, Math Comput Appl, 21 (2016) 34.

  • Banerjee A & Shan H, Encyclopedia of Machine Learning, (Springer, Boston, MA), 2011.

  • Wu H, Cheung S-F & Leung S-O, Multivariate Behav Res, 55 (2020) 1.

  • Kiruthika C & Chandrasekaran R, J Appl Statistics, 39 (2012) 1129.

  • Hamdi AA, Sayed MS, Fouad MM & Hadhoud MM, International Conference on Innovative Trends in Computer Engineering (ITCE), Aswan University, Egypt, 19-21 February 2018.

  • Pan R, Kumah C & Zhang N, Curr Trends Fashion Technol Textile Eng, 1 (2017) 79.

  • Huang ML & Fu CC, Fibers, 6 (2018) 73.

  • Hossain A & Samanta AK, J Text Eng Fashion Technol, 5 (2019) 213.

  • Fraley C & Raftery AE, J Am Stat Assoc, 97 (2002) 611.

  • Brock G, Pihur V, Datta S & Datta S, J Stat Softw, 25 (2008) 1.


Abstract Views: 138

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  • Classification of Ring-Spun Yarns Using Cluster Analysis

Abstract Views: 138  |  PDF Views: 82

Authors

Elham Naghashzargar
Department of Textile Engineering, Faculty of Engineering, University of Bonab, Bonab 5551395133, Iran, Islamic Republic of
Haleh sadat Nekoee Zahraei
Biostatistics Unit, Department of Public Health, University of Liege, Belgium

Abstract


The aim of this study is to classify ring-spun yarns according to their unevenness, imperfections, and hairiness parameters using cluster analysis. The mentioned features of ring-spun yarns are measured for five different ranks. Five ranks of ring-spun yarns including compact and conventional as well as combed and carded types are chosen and produced. In the modeling section, the model-based clustering method was applied as a strong method based on the distribution of each variable. In order to select the best fit and to find out the final clustering, bayesian information criterion (BIC) is applied. According to the results of modeling, five ranks of selected ring-spun yarns are classified in four clusters and the acceptable agreement is measured according to Cohen’s kappa method. The highest value for Kappa represents a high agreement to match between the clustering result and the real rank.

Keywords


Clustering Validation, Model-Based Clustering, Ring-Spun Yarn, Statistical Analysis.

References