Knowledge Discovery and Data Mining Techniques in Textile Industry

This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship.

Authors:

• Filiz Ersoz
• Taner Ersoz
• Erkin Guler

Keywords:

• Data mining
• textile production
• decision trees
• classification.

References:

[1] Çakır, O "Comparison of Classification Methods in Data Mining", An Application on Banking Customer Database, Ph.D. Thesis, Marmara University Institute of Social Sciences, Istanbul, 2008.
[2] Ersoz, Filiz, “Veri Madenciliği Teknikleri ve Uygulamaları”, Dijital Basımevi, 72 Tasarım, Ankara (2015).
[3] Jiawei, H., Kamber M., “Data Mining:Concepts and Techniques”, University of Simon Fraser, 2001.
[4] Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. “Knowledge Discovery and Data Mining: Towards a Unifying Framework. In Simoudis, E., Han, J., and Fayyad, U., Editors, Proceedings of KDD'96”, Second International Conference on Knowledge Discovery & Data Mining, pages 82-88, AAAI Press, Menlo Park, CA.1996
[5] Gürsoy, U. T. Ş. "Data Mining and Knowledge Discovery", Pegem Academy, Ankara (2009).