International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 4, No. 6, 8 Jun. 2012
Cover page and Table of Contents: PDF (size: 476KB)
Mining the Shirt Sizes for Indian Men by Clustered Classification
Full Text (PDF, 476KB), PP.12-27
Views: 0 Downloads: 0
Author(s)
Index Terms
Data Mining, Clustering, Classifiers, IBK KNN, Logitboost, Clothing industry, Anthropometric data
Abstract
In garment production engineering, sizing system plays an important role for manufacturing of clothing. The standards for defining the size label are a critical issue. Locating the right garment size for a customer depends on the label as an interface. In this research work intend to approach that it could be used for developing sizing systems by data mining techniques applied to Indian anthropometric dataset. We propose a new approach of two-stage data mining procedure for labelling the shirt types exclusively for Indian men. In the first stage , clustering technique applied on the original dataset, to categorise the size labels. Then these clusters are used for supervised learning in the second stage for classification. A sizing system classifies a specific population into homogeneous subgroups based on some key body dimensions. The space with these dimensions gives raise to complexity for finding uniform standards. This enables us to have an interface as a communication tool among manufacturers, retailers and consumers. This sizing system is developed for the men’s age ranges between 25 and 66 years. Main attribute happens to be the chest size as clearly visible in the data set. We have obtained classifications for men’s shirt attributes based on clustering techniques.
Cite This Paper
M. Martin Jeyasingh, Kumaravel Appavoo, "Mining the Shirt Sizes for Indian Men by Clustered Classification", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.6, pp.12-17, 2012. DOI:10.5815/ijitcs.2012.06.02
Reference
[1]Chang, C.F., 1999. “The model analysis of female body size measurement from 18 to 22, J. Hwa Gang Textile, 6: 86-94.
[2]Fan, J., W. Yu and H. Lawrance, 2004. Clothing appearance and fit: Science and technology, Woodhead Publishing Limited, Cambridge, England.
[3]Tung, Y.M. and S.S. Soong, 1994. The demand side analysis for Taiwan domestic apparel market, J. the China Textile Institute, 4: 375-380.
[4]Hsu, K.M. and S.H. Jing, 1999. The chances of Taiwan apparel industry, J. the China Textile Institute, 9: 1-6.
[5]Workman, J.E., 1991. Body measurement specification for fit models as a factor in apparel size variation, Cloth Text Res. J., 10(1): 31-36.
[6]LaBat, K.L. and M.R. Delong, 1990. Body cathexis and satisfaction with fit of apparel, Cloth Text Res. J., 8(2): 42-48.
[7]ISO 8559, 1989. Garment Construction and Anthropometric Surveys - Body Dimensions, International Organization for Standardization.
[8]R.Bagherzadeh,M.Latifi and A.R.Faramarzi, 2010,Employing a Three-Stage Data Mining Procedure to Develop Sizing System, World Applied Sciences Journal 8 (8): 923-929.
[9]G.Shakhnarovish,T. Darrell and P. Indyk, 2005, “Nearest Neighbor Methods in Learning and Vision,” MIT Press, Informatics, vol. 37, no. 6, December, 2004, pp. 461-470
[10]Tryfos, P., 1986. An integer programming approach to the apparel sizing problem, J. the Operational Research Society, 37(10): 1001-1006
[11]McCulloch, C.E., B. Paal and S.A. Ashdown, 1998. An optimal approach to apparel sizing, J. the Operational Res. Society, 49: 492-499.
[12]Gupta, D., N. Garg, K. Arora and N. Priyadarshini, 2006. Developing body measurement charts for garments manufacture based on a linear rogramming approach, J. Textile and Apparel Technology and Management, 5(1): 1-13.
[13]She, F.H., L.X. Kong, S. Nahavandi and A.Z. Kouzani, 2002. Intelligent animal fiber classification with artificial neural networks, Textile Research J., 72(7): 594-600.
[14]Moon, J.Y. and Y.N. Nam, 2003. A study the elderly women’s lower body type classification and lower garment sizing systems, Proceedings of International Ergonomics Association Conference.
[15]Hsu, C.H. and M.J. Wang, 2005. Using decision tree based data mining to establish a sizing system for the manufacture of garments, International J. Advanced Manufacturing Technol., 26(5& 6): 669-674.
[16]Meng, J.C., L. Hai and J.J.W. Mao, 2007. Thedevelopment of sizing systems for Taiwanese elementary- and high-school students, International J. Industrial Ergonomics, 37: 707-716.
[17]J. R.Quinlan, 1993, “C4.5: Programs for machine learning,” San Francisco, CA: Morgan Kaufman.
[18]Liang Xun, 2006,Data Mining:Algorithms and Application. Beijing university Press: pp.22-42.
[19]Stephan White, 2004, Enhancing the academic learning opportunities for all students. Such a plan Examination of the Meta-Analytic Evidence, in School Desegregation .. at 68, 68–72;.
[20]Bureau of Indian Standards(BIS),Indian standard size designation of clothes definition and body measurement procedure,ICS 61.020,IS 14453:1997.
[21]Winifred Aldrich,2008,Metric Pattern cutting for Menswear,Fourth Edition, Blackwell publishing, ISBN-978 1405 10278 0,10-15.