Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 6
1662-1667

Outlier detection algorithm based on affinity propagation

Zhang Qianqian1
Yu Jiong1,2
Li Ziyang2
Pu Yonglin2
1. School of Software, Xinjiang University, Urumqi 830091, China
2. College of Information Science & Engineering, Xinjiang University, Urumqi 830046, China
DOI: 10.19734/j.issn.1001-3695.2020.08.0226

Abstract

Outliers are a class of objects with different properties from other normal points, whose detection technology in various industries has a wide application to maintain the purity of data and ensure the safety of the industry. Most of the existing algorithms are based on distance, density, and other traditional methods to detect outliers. This paper assigned each object an "isolation degree", the degree of isolation of the point relative to adjacent points, which could identify outliers by sorting, that was more efficient. It proposed the detection technology APO by improving and optimizing the AP clustering algorithm. It introduced the outlier module and processed the isolated information of points. In addition, it added the amplification factor to make the difference between the outliers and the normal points more obvious. By increasing the sensitivity of the algorithm to outliers, it improved the accuracy of the algorithm. The experiment used simulated dataset real datasets, who's the results showed that the algorithm was more sensitive and it detected outliers more accurately than AP algorithm. In addition, this algorithm can cluster outliers while detecting outliers, which is not available in other detection algorithms.

Foundation Support

国家自然科学基金资助项目(61862060,61462079,61562086,61562078)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.08.0226
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 6
Section: Algorithm Research & Explore
Pages: 1662-1667
Serial Number: 1001-3695(2021)06-011-1662-06

Publish History

[2021-06-05] Printed Article

Cite This Article

张倩倩, 于炯, 李梓杨, 等. 基于近邻传播的离群点检测算法 [J]. 计算机应用研究, 2021, 38 (6): 1662-1667. (Zhang Qianqian, Yu Jiong, Li Ziyang, et al. Outlier detection algorithm based on affinity propagation [J]. Application Research of Computers, 2021, 38 (6): 1662-1667. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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