Density Peak Clustering Based on Relative Density under Progressive Allocation Strategy

被引:1
|
作者
Liu, Yongli [1 ]
Zhao, Congcong [1 ]
Chao, Hao [1 ]
机构
[1] Henan Polytech Univ, Sch Comp Sci & Technol, Jiaozuo 454003, Henan, Peoples R China
关键词
density peak clustering; progressive allocation strategy; relative density;
D O I
10.3390/mca27050084
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In traditional density peak clustering, when the density distribution of samples in a dataset is uneven, the density peak points are often concentrated in the region with dense sample distribution, which is easy to affect clustering accuracy. Under the progressive allocation strategy, a density peak clustering algorithm based on relative density is proposed in this paper. This algorithm uses the K-nearest neighbor method to calculate the local density of sample points. In addition, in order to avoid the domino effect during sample allocation, a new similarity calculation method is defined, and a progressive allocation strategy from near to far is used for the allocation of the remaining points. In order to evaluate the effectiveness of this algorithm, comparative experiments with five algorithms were carried out on classical artificial datasets and real datasets. Experimental results show that the proposed algorithm can achieve higher clustering accuracy on datasets with uneven density distribution.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] Nearest neighbors-based adaptive density peaks clustering with optimized allocation strategy
    Sun, Lin
    Qin, Xiaoying
    Ding, Weiping
    Xu, Jiucheng
    NEUROCOMPUTING, 2022, 473 : 159 - 181
  • [32] Regularized LFDA algorithm based on density peak clustering
    Tao X.
    Wu Y.
    Bao Y.
    Qi L.
    Chen W.
    Fan Z.
    Huang S.
    Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2023, 29 (11): : 3639 - 3655
  • [33] Density Peak Clustering Based Split-and-Merge
    Lu, Jixiang
    Zhong, Caiming
    ROUGH SETS, IJCRS 2022, 2022, 13633 : 191 - 202
  • [34] A Robust Density Clustering Algorithm Based on Gravity Peak
    Zhang, Rui
    Du, Tao
    Qu, Shouning
    Zhu, Lianjiang
    Wang, Xintang
    2019 6TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI 2019), 2019, : 544 - 549
  • [35] A Density Peak Clustering Algorithm Based on Information Bottleneck
    Liu, Yongli
    Zhao, Congcong
    Chao, Hao
    JOURNAL OF INFORMATION PROCESSING SYSTEMS, 2023, 19 (06): : 778 - 790
  • [36] Enhancing Density Peak Clustering via Density Normalization
    Hou, Jian
    Zhang, Aihua
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 16 (04) : 2477 - 2485
  • [37] On a two-stage progressive clustering algorithm with graph-augmented density peak clustering
    Niu, Xinzheng
    Zheng, Yunhong
    Liu, Wuji
    Wu, Chase Q.
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2022, 108
  • [38] Co-Spectral Clustering Based Density Peak
    Li, Yang
    Liu, Weifeng
    Wang, Yanjiang
    Tao, Dapeng
    2015 IEEE 16TH INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT), 2015, : 925 - 929
  • [39] DenPEHC: Density peak based efficient hierarchical clustering
    Xu, Ji
    Wang, Guoyin
    Deng, Weihui
    INFORMATION SCIENCES, 2016, 373 : 200 - 218
  • [40] Density peaks clustering based on local fair density and fuzzy k-nearest neighbors membership allocation strategy
    Ren, Chunhua
    Sun, Linfu
    Gao, Yunhui
    Yu, Yang
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2022, 43 (01) : 21 - 34