Document Type : Original Article
Authors
1 Ph.D. Student of Industrial Management, Faculty of Management and Economics, Branch of Science and Research, Islamic Azad University, Tehran, Iran.
2 Associate Professor, Faculty of Management and Economics, Branch of Science and Research, Islamic Azad University, Tehran, Iran.
3 Professor, Management and Accounting Faculty, Tarbiat Modares University, Tehran, Iran.
4 Associate professor, Faculty of management, Tehran University, Tehran, Iran.
Abstract
Predictive approaches play an important role in detecting events, controlling risks and reducing maintenance and repair costs. The purpose is to provide a model for predicting critical and prioritized risks based on data mining algorithms. Data mining method was planned based on the CRISP methodology. Data modeling has been done in two parts: "descriptive" and "predictive" data mining and the use of "clustering" and "classification" algorithms."Sillhouette index" is considered for clustering and the K-Means, Kohnen, Two Step algorithm is used; the best value is based on the K-Means algorithm. Silhouette is equal to 0.6446 with the number of clusters 5. Next, Neural Network Algorithms, C.5 tree, Nearest Neighbor and Support Vector have been used for classification. These techniques recognizing data classification patterns and their integration increases the amount of data learning. The results showed learning in 97.56% of the agreed data and the accuracy and validity of the combined model for data classification was estimated at 92.86%. Based on the results, 13 critical risks have been identified; "release of polluting gases and chemicals" and "lack of training and justification of contractors regarding the pipeline" have the highest and lowest priority, respectively.
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Main Subjects
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