Improved Harris Combined With Clustering Algorithm for Data Traffic Classification

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