Cover page and Table of Contents: PDF (size: 462KB)
Full Text (PDF, 462KB), PP.34-39
Views: 0 Downloads: 0
Machine learning, Data mining, Enterprise resource planning, Data warehouse
Nowadays, there exists a lot of information that can be handled from business transactions and scientific data and information retrieval is simply no longer enough for decision-making. In this paper will supervised machine learning technique is applied to the mine data warehouse for Enterprise Resource Planning (ERP) of the General Electricity Company of Libya (GECOL). This technique has been applied for the first time on the data of production, transportation and distribution departments. These data are in the form of purchase and work orders of operational material strategic equipment spare parts. This technique would extract prediction rules in order to assist the decision-makers of the company to make appropriate future decisions more easily and in less time. A supervised machine learning technique has been adopted and applied for the mining data warehouse. A well-known software package for data mining which is referred to as WEKA tool was adopted throughout this work. The WEKA tool is applied to the collected data from GECOL. The conducted experiments produce prediction models in the form set of rules in order to help responsible employees make the suitable, right and accurate future decision in a simple way and inappropriate time. The collected data were preprocessed to be prepared in a suitable format to be fed to the WEKA system. A set of experiments has been conducted on those data to obtain prediction models. These models are in the form of decision rules. The produced models were evaluated in terms of accuracy and production time. It can be concluded that the obtained results are very promising and encouraging.
Ashraf Mohammed Abusida, Yasemin Gültepe, "An Association Prediction Model: GECOL as a Case Study", International Journal of Information Technology and Computer Science(IJITCS), Vol.11, No.10, pp.34-39, 2019. DOI:10.5815/ijitcs.2019.10.05
W. Alsuessi, “General Electricity Company of Libya (GECOL), European International Journal of Science and Technology, Vol. 4 No. 1, 2015.
D. M. Bahssas, A. M. AlBar and R. Hoque, “Enterprise Resource Planing (ERP) Systems: Design, Trends and Deployment”, The International Technology Management Review, Vol. 5, No. 2, pp. 72-81, 2015.
T. Slimani and A. Lazzez, “Efficient Analysis of Pattern and Association Rule Mining Approaches”, International Journal of Information Technology and Computer Science, Vol. 6, No. 3, pp. 70-81, 2014.
P. Giudici and S. Figini, “Applied Data Mining for Business and Industry”, Second Edition, Wiley.
M. Zhou and T. Wang, “Fault Diagnosis of Power Transformer Based on Association Rules Gained by Rough Set”, The 2nd International Conference on Computer and Automation Engineering, 2010.
V. Ivančević, I. Tušek, M. Knežević, S. Elheshk and I. Luković, “Using Association Rule Mining to Identify Risk Factors for Early Childhood Caries”, Computer Methods And Programs In Biomedicine , vol. 122, pp. 175–181, 2015.
L. Li, X. Longjun, Z. Deng, Y. Bin, G. Yafeng and L. Fuchang, “Condition Assessment of Power Transformers using a Synthetic Analysis Method Based on Association Rule and Variable Weight Coefficients”, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 20, 2013.
J. R. Daher, S. Chilkaka, A. Younes and K. Shaban, “Association Rule Mining Five Years of Motor Vehicle Crashes”, 5th International Conference on Transportation and Traffic Engineering, 2016.
H. Oliff and Y. Liu, “Towards Industry 4.0 Utilizing Data-Mining Techniques: A Case Study on Quality Improvement”, The 50th CIRP Conference on Manufacturing Systems, 2017.
N. Liu and L. Ma, “Research of Improved Apriori Algorithm Based on Itemset Array”, Sensors and Transducers, Vol. 153, No. 6, pp. 84-91, 2013.
B. Rokaha and D. P. Ghale, “Enhancement of Supermarket Business and Market Plan by Using Hierarchical Clustering and Association Mining Technique”, International Conference on Networking and Network Applications, 2018.
R. Ismail, Z. Othman and A. A. Bakar, “Associative Prediction Model and Clustering for Product Forecast Data”, 10th International Conference on Intelligent Systems Design and Applications, 2010.
I. H. Witten, E. Frank and M. A. Hall, “Data Mining Practiccal Machine Learning Tools and Techniques”, Second Edition, Elsevier, 2005.
R. R. Bouckaert, E. Frank, M. Hall, R. Kirkby, P. Reutemann, A. Seewald and D. Scuse, “WEKA Manual”, Version 3-6-10, University of Waikato, 2013.
K. Mani and R. Akila, “Enhancing the Performance in Generating Association Rules using Singleton Apriori”, International Journal Of Information Technology and Computer Science, Vol. 9, No. 1, pp. 58-64, 2017.