Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 3
667-671

Improved density peak clustering algorithm based on weighted K-nearest neighbor

Yang Zhen
Wang Hongjun
National University of Defense Technology, Hefei 230037, China
DOI: 10.19734/j.issn.1001-3695.2018.08.0656

Abstract

The density peak clustering algorithm was a new density-based clustering algorithm, the algorithm requires only one input parameter and does not require frequent iterative processes. However, the original algorithm only considers the global structure of the data, and the effect is not ideal when clustering data sets with uneven distribution. Moreover, the original algorithm only selects the cluster center according to the distribution of points on the decision graph, which is not reliable. Aiming at the above problems, this paper proposed an improved density peak clustering algorithm based on weighted K-nearest neighbor. It introduced the idea of nearest neighbor algorithm into the density peak clustering algorithm, refined and calculated the local density of each data point, and determined the critical point of the cluster center by the trend of the slope of the weight. The improved algorithm was compared with the original density peak clustering algorithm, K-means algorithm and DBSCAN algorithm by experiments on the artificial dataset and UCI real dataset. It was proved that the improved algorithm can deal with the density uneven dataset and find clusters of arbitrary shapes. On the three cluster performance indicators, the improved algorithm is generally higher than the other three algorithms.

Foundation Support

国家自然科学基金资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.08.0656
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 3
Section: Algorithm Research & Explore
Pages: 667-671
Serial Number: 1001-3695(2020)03-006-0667-05

Publish History

[2020-03-05] Printed Article

Cite This Article

杨震, 王红军. 基于加权K近邻的改进密度峰值聚类算法 [J]. 计算机应用研究, 2020, 37 (3): 667-671. (Yang Zhen, Wang Hongjun. Improved density peak clustering algorithm based on weighted K-nearest neighbor [J]. Application Research of Computers, 2020, 37 (3): 667-671. )

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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