Improved Harris Combined With Clustering Algorithm for Data Traffic Classification

Aiming at the problem that the data traffic in the intelligent wireless communication system presents complex characteristics such as burstiness and self-similarity, which leads to the low classification accuracy of the existing classification model for traffic, a data traffic classification method based on improved Harris Eagle algorithm combined with fuzzy C-means clustering is proposed. The method maps traffic samples to Harris Eagle individuals, finds the optimal position through multiple iterations of the algorithm, and uses this as the initial center point of the clustering algorithm to guide data traffic classification. The simulation shows that, compared with the traditional fuzzy clustering method, the clustering method based on the particle swarm algorithm and the gray wolf algorithm, the improved Harris Eagle combined with fuzzy clustering has better intra-class compactness and inter-class separation on the data traffic sample set. Meanwhile, the clustering accuracy and recall rate are both improved to about 90 percent.

Automated summary

This study proposes a data traffic classification method based on an improved Harris Eagle algorithm combined with fuzzy C-means clustering. By mapping traffic samples to Harris Eagle individuals and finding the optimal position through multiple iterations, the method guides data traffic classification. Experimental results demonstrate that this method achieves better accuracy and recall rate in classifying data traffic samples.