Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (2): 692-696.doi: 10.13229/j.cnki.jdxbgxb20200045

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Local outlier data mining based on artificial intelligence technology

Fu-hua SHANG1(),Mao-jun CAO1(),Cai-zhi WANG2   

  1. 1.School of Computer & Information Technology,Northeast Petroleum University,Daqing 163318,China
    2.Department of Well Logging & Remote Sensing Technology,Research Institute of Petroleum Exploration and Development,PetroChina,Beijing 100083,China
  • Received:2020-01-14 Online:2021-03-01 Published:2021-02-09
  • Contact: Mao-jun CAO E-mail:shangfh@163.com;caomaojun@126.com

Abstract:

In view of the huge memory consumption of traditional discrete data mining methods, this paper proposes a local outlier data mining method based on artificial intelligence technology. The feature of the discrete data is extracted and the local outlier data is detected by the algorithm based on information entropy. Through standardized processing of the detected data, the mining of local outlier data in neural network is realized, and the research on local outlier data mining method based on artificial intelligence technology is completed. Experimental results show that, compared with the traditional data mining method, the proposed method consumes less memory in the process of data mining and has obvious advantages, which fully verifies the applicability and effectiveness of this method.

Key words: artificial intelligence technology, local outlier data, mining method, neural network

CLC Number: 

  • TP311

Fig.1

Flow of neural network mining local discrete data"

Table 1

Parameters of experimental data set"

编号数据个数数据维数标记的子集数备注
1104010115%离群点
2360016105%离群点
3410016265%离群点
436002011%离群点
5238511010%离群点
61733238%离群点
734805415%离群点
8800071620%离群点

Table 2

Parameters of computer virtual simulation platform"

项目参数说明
CPUIntel i7-8700,3.75 GHz运行数据挖掘方法
硬盘256 GB 固态硬盘存储实验数据集合
显卡NVIDIA显示实验结果
内存16 GB存储数据挖掘方法运行时的缓存数据
操作系统Windows 8.1辅助数据挖掘方法运行

Fig.2

Comparison of memory consumption of different data mining methods"

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