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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 9
2673-2677,2682

Clustering algorithm for uncertain data stream based on damped sliding window

Tu Li1
Chen Ling2,3
1. Dept. of Computer Science, Jiangyin Polytechnic College, Jiangyin Jiangsu 214405, China
2. College of Information Engineering, Yangzhou University, Yangzhou Jiangsu 225127, China
3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
DOI: 10.19734/j.issn.1001-3695.2021.01.0015

Abstract

In view of the fact that uncertain data stream had the characteristics of non-convex distribution and contained a lot of noise, this paper proposed an algorithm Clu_Ustream for clustering uncertain data stream which solved the problem of real-time and efficient clustering evolution for recent data. Firstly, in the online part, Clu_Ustream used the sub window sampling mechanism to collect the uncertain stream data in the sliding window. Moreover, it used a double-layer summary statistical structure linked list to store the statistical information of the probability density grids to improve the processing efficiency. Secondly, in the off-line part, it used the damped window mechanism to weaken the influence of old data and deleted regularly the expired sub windows to ensure the effectiveness of clustering. In addition, it developed a dynamic abnormal grids deletion mechanism to filter most of outliers in order to dramatically improve the space and time efficiency. The experimental results on the synthetic and real datasets show that Clu_Ustream has superior clustering quality and efficiency than other similar algorithms.

Foundation Support

国家自然科学基金项目(61702441)
江苏省自然科学基金项目(BK20201430)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.01.0015
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 9
Section: Algorithm Research & Explore
Pages: 2673-2677,2682
Serial Number: 1001-3695(2021)09-020-2673-05

Publish History

[2021-09-05] Printed Article

Cite This Article

屠莉, 陈崚. 衰减窗口中的不确定数据流聚类算法 [J]. 计算机应用研究, 2021, 38 (9): 2673-2677,2682. (Tu Li, Chen Ling. Clustering algorithm for uncertain data stream based on damped sliding window [J]. Application Research of Computers, 2021, 38 (9): 2673-2677,2682. )

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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