Application of Data Mining Techniques for the Investigation of Factors Affecting Transportation Enterprises

Inland transportation, due to the importance of this business and competition between active organizations in this field, applying new technologies in management and making better decisions can be beneficial. We have presented three data mining techniques of clustering, association rules, and classification to investigate the factors affecting the cost and time of road and rail transportation. Using methods based on the K-means algorithm with comparing four clusterings, we have proposed Naive Bayes (probabilistic) classification to determine the total accuracy of transportation percent to 97.91%. Finally, classification tree algorithms such as Bayesian theory and random forest have been used, and the results and output rules have been compared. This article is comprehensive and new to use various effective parameters inland transportation. We will confirm its efficiency by using the criterion(5v)(which we will explain in its place) and then the results in the field. A larger one, called land transit, could be generalized between the two countries. In the end, we have discussed more in methodology and results