Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 2
450-455,462

Multi-perspective trace clustering method based on activity behavior relation and association time

Zhang Shuna
Fang Huana,b
a. College of Mathematics & Big Data, b. Anhui Province Engineering Laboratory for Big Data Analysis & Early Warning Technology of Coal Mine Safety, Anhui University of Science & Technology, Huainan Anhui 232001, China
DOI: 10.19734/j.issn.1001-3695.2022.07.0352

Abstract

Most of the commonly used trace clustering methods use a relatively single standard, such as using the activity sequence relationship, while ignoring the activity behavior relationship, time or resource attributes, which is unfavorable for some flexibly configured business process systems, as it hard to improve the quality of process mining. In order to solve such problems, this paper proposed a multi-perspective trace clustering method based on activity behavior relationship and association time. Firstly, this method constructed the control flow code according to the behavior relationship between activities. At the same time, in terms of time attributes, it used a group of nearest association activity pairs and their time differences to represent traces. Secondly, it used weighted aggregation to integrate the trace similarity under the two perspectives, and then adjusted the clustering results. Finally, the paper applied this method in the login system scenario and compared with other clustering methods on five real logs. The experimental results show that the method can find process scenarios from complex login systems, and verify the superiority of the method from three metrics of fitness, precision and F1 score.

Foundation Support

国家自然科学基金资助项目(61902002)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.07.0352
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 2
Section: Algorithm Research & Explore
Pages: 450-455,462
Serial Number: 1001-3695(2023)02-023-0450-06

Publish History

[2022-09-29] Accepted Paper
[2023-02-05] Printed Article

Cite This Article

张顺, 方欢. 基于活动的行为关系与关联时间的多视角迹聚类方法 [J]. 计算机应用研究, 2023, 40 (2): 450-455,462. (Zhang Shun, Fang Huan. Multi-perspective trace clustering method based on activity behavior relation and association time [J]. Application Research of Computers, 2023, 40 (2): 450-455,462. )

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.

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