Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (8): 1910-1922.DOI: 10.3778/j.issn.1673-9418.2111138

• Theory and Algorithm • Previous Articles     Next Articles

Density-Peak Clustering Algorithm on Decentralized and Weighted Clusters Merging

ZHAO Liheng1,+(), WANG Jian1,2, CHEN Hongjun1   

  1. 1. Department of Electronic Information Engineering, Chengdu Jincheng College, Chengdu 611731, China
    2. School of Computer, Sichuan University, Chengdu 610041, China
  • Received:2021-11-30 Revised:2022-03-24 Online:2022-08-01 Published:2022-08-19
  • About author:ZHAO Liheng, born in 1976, M.S., senior engineer, professional member of CCF. His research interests include data mining and massive data storage.
    WANG Jian, born in 1979, Ph.D., associate professor. His research interests include artificial intelligence and data mining.
    CHEN Hongjun, born in 1979, M.S., professor. Her research interests include big data and artificial intelligence.
  • Supported by:
    the Collaborative Education Project of Ministry of Education of China(201902005069);the Key Research and Development Project of Sichuan Provincial Science and Technology Department(22ZDYF0724)


赵力衡1,+(), 王建1,2, 陈虹君1   

  1. 1. 成都锦城学院 电子信息学院,成都 611731
    2. 四川大学 计算机学院,成都 610041
  • 通讯作者: +E-mail:
  • 作者简介:赵力衡(1976—),男,四川成都人,硕士,高级工程师,CCF专业会员,主要研究方向为数据挖掘、海量数据存储。
  • 基金资助:


The clustering by fast search and find of density peaks (DPC) is a density-based clustering algorithm proposed in recent years, which has the advantages of simple principle, no iteration and clustering of arbitrary shape. However, the algorithm still has some defects: clustering around clustering centers makes the clustering results significantly affected by central points, and the number of clustering centers needs to be manually specified; the cutoff distance considers the distribution density of the data but ignores the internal features; if there is a sample allocation error in the clustering process, the subsequent sample clustering may amplify the error. To solve the above problems, this paper proposes a density-peak clustering algorithm on decentralized and weighted clusters merging (DCM-DPC). This algorithm introduces the weight to redefine the local density, dividing core sample groups located in different local high density regions to replace cluster centers as the cluster basis. Finally, the remaining samples are assigned to the highest coupled core sample groups or labeled as discrete points by their near neighbor samples. Experiments on artificial and UCI datasets show that the clustering performance of the proposed algorithm outperforms the contrast algorithms, and the boundary samples partition of the entangled clusters is more accurate.

Key words: density peaks, clustering, decentralized, neighborhood, clusters merging



关键词: 密度峰值, 聚类, 去中心点, 邻域, 簇归并

CLC Number: