基于深度集成学习的社交网络异常数据挖掘算法

doi:10.13229/j.cnki.jdxbgxb20211263

Abstract

Abstract:

In the current design process of social network abnormal data mining algorithm， social network data features are not extracted， resulting in low abnormal data detection rate and long detection running time. Therefore， a social network abnormal data mining algorithm based on deep integrated learning is designed. Mining social network data， extracting social network data features according to the data mining results， using the extracted data features to build a deep integrated learning model， using the model to predict abnormal data， so as to obtain abnormal social network data， and realize abnormal social network data mining. Experimental results show that the algorithm has high accuracy and effectiveness and good practical application effect through abnormal data detection rate test and abnormal data detection running time test.

Key words: deep integrated learning, social network, abnormal data mining, feature extraction

CLC Number:

TP748

Li-can DAI,Xiang DAI,Ying CUI,Yong-chao WEI. Anomaly data mining algorithm in social network based on deep integrated learning[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2712-2717.

Figures/Tables 2

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Anomaly data mining algorithm in social network based on deep integrated learning

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