Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (9): 2163-2176.DOI: 10.3778/j.issn.1673-9418.2102021

• Theory and Algorithm • Previous Articles    

Weighted K-nearest Neighbors and Multi-cluster Merge Density Peaks Clustering Algorithm

CHEN Lei, WU Runxiu(), LI Peiwu, ZHAO Jia   

  1. School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  • Received:2021-02-05 Revised:2021-04-02 Online:2022-09-01 Published:2021-04-19
  • About author:CHEN Lei, born in 1997, M.S. candidate. His research interest is data mining.
    WU Runxiu, born in 1971, professor, M.S. supervisor. Her research interests include swarm intelligence algorithm and application.
    LI Peiwu, born in 1963, Ph.D., professor, M.S. supervisor. His research interests include computer security and big data analysis.
    ZHAO Jia, born in 1981, Ph.D., professor, M.S. supervisor. His research interests include data mining and swarm intelligence algorithm.
  • Supported by:
    Science and Technology Project of Jiangxi Province Department of Education(GJJ180940);National Natural Science Foundation of China(52069014);National Natural Science Foundation of China(51669014);Science Fund for Distinguished Young Scholars of Jiangxi Province(2018ACB21029)


陈磊, 吴润秀(), 李沛武, 赵嘉   

  1. 南昌工程学院 信息工程学院,南昌 330099
  • 通讯作者: + E-mail:
  • 作者简介:陈磊(1997—),男,硕士研究生,主要研究方向为数据挖掘。
  • 基金资助:


Density peaks clustering (DPC) algorithm is a clustering algorithm based on density. The algorithm is simple in principle and efficient in operation, and can find any non-spherical class clusters. However, there are some defects in the algorithm. Firstly, the measurement criteria defined by the local density are not uniform and there are great differences in the clustering results. Secondly, the allocation strategy is prone to allocation errors, that is once a sample is incorrectly allocated, a series of subsequent samples will be incorrectly allocated too. In order to solve these problems, this paper proposes a weighted K-nearest neighbors and multi-cluster merge density peaks clustering (WKMM-DPC) algorithm. Combined with the idea of weighted K-nearest neighbors, the local density of the sample is redefined by introducing the weight coefficient of the sample, which makes the local density more dependent on the position of the sample in the K-nearest neighbors, and unifies the measurement criteria of density definition. The similarity between clusters is defined, and the clusters are merged according to the metric to avoid the joint error in the allocation of remaining samples. Experiments on artificial and UCI datasets show that the clustering performance of the proposed algorithm is better than that of FKNN-DPC, DPCSA, FNDPC, DPC and DBSCAN algorithms.

Key words: clustering, local density, density peaks, K-nearest neighbors (KNN), multi-cluster merge



关键词: 聚类, 局部密度, 密度峰值, K近邻(KNN), 多簇合并

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