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Research on network abnormal data flow mining based on improved cluster analysis

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Abstract

Aiming at the problems of traditional methods that cannot adapt to the interference of noise or abnormal data, the data mining time is long, and the data mining accuracy is low, a network abnormal data stream mining method based on improved clustering analysis is proposed. By establishing a preprocessing model for abnormal network data flow, real-time data flow query is realized. Construct a network abnormal incremental data classification model to reduce the interference of noise data on data processing. The least square method is used to further filter the interference data in the abnormal incremental data of the network, and obtain the quantized data stream. Statistic network abnormal data frequent pattern data set, on this basis, adopt improved clustering method to complete the mining of network abnormal data stream. The experimental results show that the highest anti-noise coefficient of the proposed method is 0.7, and the data mining time is shorter, and the data mining accuracy is higher, which fully verifies the data stream mining performance of the method.

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Authors and Affiliations

  1. School of Computer, Weinan Normal University, Weinan, Shaanxi, China

    Xiaoqiang Jia

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  1. Xiaoqiang Jia
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Jia, X. Research on network abnormal data flow mining based on improved cluster analysis. Distrib Parallel Databases 40, 797–813 (2022). https://doi.org/10.1007/s10619-021-07353-y

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