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.
| Published in | Industrial Engineering (Volume 2, Issue 1) |
| DOI | 10.11648/j.ie.20180201.14 |
| Page(s) | 26-33 |
| 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), 2018. Published by Science Publishing Group |
Data Mining Algorithms, Classification, Industrial Data, Accuracy, ROC Curve
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APA Style
Hamza Saad. (2018). The Application of Data Mining in the Production Processes. Industrial Engineering, 2(1), 26-33. https://doi.org/10.11648/j.ie.20180201.14
ACS Style
Hamza Saad. The Application of Data Mining in the Production Processes. Ind. Eng. 2018, 2(1), 26-33. doi: 10.11648/j.ie.20180201.14
AMA Style
Hamza Saad. The Application of Data Mining in the Production Processes. Ind Eng. 2018;2(1):26-33. doi: 10.11648/j.ie.20180201.14
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Download@article{10.11648/j.ie.20180201.14,
author = {Hamza Saad},
title = {The Application of Data Mining in the Production Processes},
journal = {Industrial Engineering},
volume = {2},
number = {1},
pages = {26-33},
doi = {10.11648/j.ie.20180201.14},
url = {https://doi.org/10.11648/j.ie.20180201.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ie.20180201.14},
abstract = {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.},
year = {2018}
}
TY - JOUR T1 - The Application of Data Mining in the Production Processes AU - Hamza Saad Y1 - 2018/10/30 PY - 2018 N1 - https://doi.org/10.11648/j.ie.20180201.14 DO - 10.11648/j.ie.20180201.14 T2 - Industrial Engineering JF - Industrial Engineering JO - Industrial Engineering SP - 26 EP - 33 PB - Science Publishing Group SN - 2640-1118 UR - https://doi.org/10.11648/j.ie.20180201.14 AB - 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. VL - 2 IS - 1 ER -
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