The Use of Data Mining Methods for Predicting Breakdowns of Data Transmission Network Devices
Discusses two methods for predicting network equipment failures. The purpose of the article is to reveal the relevance of the approach proposed by the authors to the use of large-volume data in the chosen method of machine learning and to make a comparative analysis of the final values with the works of world researchers. In the first section, the authors analyze the work of scientists from the Beijing University of Post and Telecommunications, noting the strengths and weaknesses of their method. In section 2, the authors applied the random tree method in modeling machine learning methods of RapidMiner studio. The authors carried out work with a large amount of data and conducted a comparative analysis of the results of modeling by the method. The importance of using models and methods for predicting network equipment failures is proved. In the final section, the authors emphasize the need to improve forecasting models for their further implementation in the working environment. The authors also emphasize that the chosen method for predicting failures in the local network system of scientists from Beijing University of Posts and Telecommunications is a special case, as well as the method proposed by the authors of this article.