The improved clustering algorithm is used to analyze the data anomalies in the network environment

Abstract

In this paper, an improved clustering algorithm is proposed and a heterogeneous model based on this model is developed. A new data extraction technology, such as data classification, network platform anomaly detection, distributed maximum frequent sequence extraction, comparison and mining of maximum frequent sequence data, is adopted. Through the comparison experiment, it is found that the algorithm can better reflect the correlation with the corresponding abnormal data types, and can better reflect the actual use of the algorithm.