The Application of Data Mining in the Production Processes
Industrial Engineering
Volume 2, Issue 1, June 2018, Pages: 26-33
Received: Sep. 16, 2018; Accepted: Sep. 28, 2018; Published: Oct. 30, 2018
Views 212      Downloads 53
Hamza Saad, System Science and Industrial Engineering, Binghamton University, New York, USA
Article Tools
Follow on us
Traditional statistical and measurements are unable to solve all industrial data in the right way and appropriate time. Open markets mean the customers are increased, and production must increase to provide all customer requirements. Nowadays, large data generated daily from different production processes and traditional statistical or limited measurements are not enough to handle all daily data. Improve production and quality need to analyze data and extract the important information about the process how to improve. Data mining applied successfully in the industrial processes and some algorithms such as mining association rules, and decision tree recorded high professional results in different industrial and production fields. The study applied seven algorithms to analyze production data and extract the best result and algorithm in the industry field. KNN, Tree, SVM, Random Forests, ANN, Naïve Bayes, and AdaBoost applied to classify data based on three attributes without neglect any variables whether this variable is numerical or categorical. The best results of accuracy and area under the curve (ROC) obtained from Decision tree and its ensemble algorithms (Random Forest and AdaBoost). Thus, a decision tree is an appropriate algorithm to handle manufacturing and production data especially this algorithm can handle numerical and categorical data.
Data Mining Algorithms, Classification, Industrial Data, Accuracy, ROC Curve
To cite this article
Hamza Saad, The Application of Data Mining in the Production Processes, Industrial Engineering. Vol. 2, No. 1, 2018, pp. 26-33. doi: 10.11648/
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Saad HR (2018), “Use Bagging Algorithm to Improve Prediction Accuracy for Evaluation of Worker Performances at a Production Company”. Ind Eng Manage 7: 257. doi:10.4172/2169-0316.1000257.
Han, J., and Kamber, M., 2001, “Data Mining: Concepts and Techniques, Morgan Kaufmann”, New York, 550 pp.
Malkoff, D. B., (1987), “A Framwork for Real-Time Fault Detection and Diagnosis Using Temporal Data,” Artif. Intell. Eng., 22, pp. 97–111.
Ramamoorthy, C. V., and Wah, B. W., (1989), “Knowledge and Data Engineering,” IEEE Trans. Knowl. Data Eng., 11, pp. 9–16.
Lee, M. H., (1993), “Knowledge Based Factory,” Artif. Intell. Eng., 8, pp.109–125.
Irani, K. B., Cheng, J., Fayyad, U. M., and Qian, Z., (1993), “Applying Machine Learning to Semiconductor Manufacturing,” IEEE Expert, 81, pp. 41–47.
Piatetsky-Shapiro, G., (1999), “The Data Mining Industry Coming of Age,” IEEE Intell. Syst., 146, pp. 32–34.
Foguem, Rigal, & Mauget (2013). Mining Association Rules for the Quality Improvement of the production process. Expert system with applications journal.
Saed Sayad (2010-2018) “An Introduction to Data Science”. Copyright © 2010-2018, Dr. Saed Sayad.
J. A. Harding, M. Shahbaz, Srinivas, A. Kusiak (2006) Data Mining in Manufacturing: A Review. Journal of Manufacturing Science and Engineering. DOI: 10.1115/1.2194554.
H Saad and N Nagarur (2017), “Data Analysis of Early Detection and Clinical Stages of Breast Cancer in Libya”. The 6th Annual World Conference of the Society for Industrial and Systems Engineering.
Science Publishing Group
NEW YORK, NY 10018
Tel: (001)347-688-8931