Network anomaly data mining and classification method based on improved K-means clustering algorithm
In order to solve the problem of high false positive rate and false positive rate in the process of network abnormal data mining,this paper proposes a method of network abnormal data mining and classification based on improved K-means clustering algorithm.A parallel frequent itemset mining environment is constructed to accelerate data processing,and local outlier detection is used to eliminate outliers.K-means clustering was introduced to calculate the maximum and minimum distance of the data,and membership function and density peak optimization algorithm were integrated to improve the initial center selection and cluster boundary adjustment of the cluster,so as to improve the accuracy of anomaly recognition and classification efficiency.The experimental results show that the clustering effect and performance of the proposed method are obviously improved.
万方数据
维普
