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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1058-1063

Density peak clustering algorithm based on grid neighbor optimization

Liu Jia,b
Yang Jinruia
a. School of Statistics & Data Science, b. Xinjiang Social & Economic Statistics & Big Data Application Research Center, Xinjiang University of Finance & Economics, rümqi 830012, China
DOI: 10.19734/j.issn.1001-3695.2023.08.0396

Abstract

Density peak clustering(DPC) combines the local density and relative distance of data sample points, and can cluster arbitrary shaped datasets. However, DPC algorithm has problems such as subjective selection of truncation distance, simple allocation strategy, and high time complexity. This paper proposed a density peak clustering algorithm based on grid nearest neighbor optimization(KG-DPC algorithm). Firstly, it gridded the data space to reduce the computational burden of distance between sample data points. When calculating local density, not only the density values of the grid itself were consi-dered, but also the density values of the surrounding k nearest neighbors were considered, reducing the impact of subjective selection of truncation distance on clustering results, improving clustering accuracy, and setting a grid density threshold to ensure the stability of clustering results. The experimental results show that the KG-DPC algorithm has a significant improvement in clustering accuracy compared to DBSCAN, DPC, and SDPC algorithms. Compared to DPC, SNN-DPC, and DPC-NN algorithms, the average consumption time in clustering is reduced by 38%, 44% and 44%, respectively. On the basis of ensuring the accuracy of basic clustering, the KG-DPC algorithm has specific advantages in clustering efficiency.

Foundation Support

国家自然社科基金资助项目(72164034)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.08.0396
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: Algorithm Research & Explore
Pages: 1058-1063
Serial Number: 1001-3695(2024)04-015-1058-06

Publish History

[2023-12-11] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

刘继, 杨金瑞. 基于网格近邻优化的密度峰值聚类算法 [J]. 计算机应用研究, 2024, 41 (4): 1058-1063. (Liu Ji, Yang Jinrui. Density peak clustering algorithm based on grid neighbor optimization [J]. Application Research of Computers, 2024, 41 (4): 1058-1063. )

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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