A Local Outlier Detection Method Based on Objective Function

被引：0
|
作者：
Zhou Y. [1 ]
Zhu W.-H. [1 ]
Sun H.-Y. [1 ]
机构：
[1] School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2022年 / 43卷 / 10期
关键词：
fuzzy C-means (FCM) algorithm; local outlier factor(LOF); objective function; outlier detection; pruning;
D O I：
10.12068/j.issn.1005-3026.2022.10.006
中图分类号：
学科分类号：
摘要：
The traditional density based local outlier detection algorithm does not preprocess the original data set, which leads to the unsatisfactory detection effect when facing the unknown data set. Moreover, due to the need to calculate the outlier factor of each data point, the amount of calculation increases greatly when the amount of data is too large. Through the analysis of the local outlier detection algorithm, a local outlier detection method based on objective function FOLOF(FCM objective function-based LOF) is proposed. Firstly, the elbow rule is used to determine the optimal number of clusters in the data set. Then, the data set is pruned by the objective function of FCM to obtain the outlier candidate set. Finally, the weighted local outlier factor detection algorithm is used to calculate the outlier degree of each point in the candidate set. The relevant experiments are carried out on the artificial data set and UCI data sets. At the same time, the proposed method is compared with other methods. The results show that the proposed algorithm can improve the outlier detection accuracy, reduce the computational cost, and effectively achieve a better performance. © 2022 Northeastern University. All rights reserved.
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页码：1405 / 1412
页数：7
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