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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 7
1969-1973

Outlier detection algorithm based on neighborhood system density difference measurement

Du Xusheng1a
Yu Jiong1a,1b
Chen Jiaying1b
Wang Yuefei1b
Pu Yonglin1b
Ye Lele2
1. a. School of Software, b. School of Information Science & Engineering, Xinjiang University, Urumqi 830008, China
2. School of Software, Xi'an Jiaotong University, Xi'an 710049, China
DOI: 10.19734/j.issn.1001-3695.2018.12.0932

Abstract

LOF is a famous algorithm for outlier detection, and it has lower detection accuracy and higher parameter sensitivity on high-dimensional discrete distribution datasets. Aiming at these problems, this paper proposed a neighborhood system density difference(NSD) algorithm based on density difference measurement of neighborhood systems. Compared with the traditional density-based methods, NSD algorithm proposed and introduced the concept of intercept distance. Firstly, it calculated the number of neighbors of an object within the intercept distance on dataset. Then, it computed the density of neighborhood system. After that, it estimated the degree of tending to the same cluster by comparing the density between the object and its neighbors. Finally, it output the objects which closed to outlier with maximum likelihood. Experiments with NSD, LOF, LDOF, CBOF algorithms carried out on the real-world dataset and synthetic dataset, show that the NSD algorithm performs with higher detection accuracy and execution efficiency, while with lower parameter sensitivity.

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.1001-3695.2018.12.0932
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 7
Section: Algorithm Research & Explore
Pages: 1969-1973
Serial Number: 1001-3695(2020)07-010-1969-05

Publish History

[2020-07-05] Printed Article

Cite This Article

杜旭升, 于炯, 陈嘉颖, 等. 一种基于邻域系统密度差异度量的离群点检测算法 [J]. 计算机应用研究, 2020, 37 (7): 1969-1973. (Du Xusheng, Yu Jiong, Chen Jiaying, et al. Outlier detection algorithm based on neighborhood system density difference measurement [J]. Application Research of Computers, 2020, 37 (7): 1969-1973. )

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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