Strategy of Big Data Clustering Algorithms in Network Information Security

For massive data, traditional clustering methods often require repeated iterations and calculations, which consume a lot of time and resources. Therefore, this article chooses to use big data clustering algorithms to compare relevant strategies in network information security for analysis, in order to provide reference for research in related fields. This article compares the performance of the algorithm used with other algorithms in terms of abnormal response time and abnormal detection rate through experiments. The average experimental results of the algorithm selected in this article are 13.31 seconds and 91.1%, respectively, which are far superior to the other two algorithms. The results indicate that the experiment in this article focuses on the close combination of theory and practice, which improves the efficiency of the algorithm in real scenarios.