Density peak clustering based on improved dung beetle optimization and mahalanobis metric

被引:4
|
作者
Zhang, Hang [1 ]
Liu, Yongli [1 ]
Chao, Hao [1 ]
机构
[1] Henan Polytech Univ, Sch Comp Sci & Technol, Jiaozuo, Henan, Peoples R China
关键词
Density peak clustering; nonlinear dynamic factor; adaptive cosine wave inertia weight; mahalanobis metric; FAST SEARCH; ALGORITHM; FIND;
D O I
10.3233/JIFS-232334
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The density peak clustering algorithm (DPC) quickly divides each cluster based on high-density peak points and shows better clustering performance. In order to address the issue that the local density is constrained by the preset cut-off distance in DPC and the Euclidean distance cannot capture the possible correlation between different features, a DPC algorithm based on improved dung beetle optimization (IDBO) and Mahalanobis metric is proposed, called IDBO-MDDPC. The IDBO algorithm enhances the ball dung beetle individual by incorporating nonlinear dynamic factors to increase the search and development capabilities of the algorithm and by incorporating an adaptive cosine wave inertial weight strategy to more precisely determine the optimal position of the thief dung beetle in order to improve the convergence speed and accuracy of the algorithm. The IDBO algorithm is simulated on eight benchmark functions, and the results demonstrate that it is superior to other comparison algorithms in terms of convergence speed and accuracy. In the DPC algorithm, the Mahalanobis metric is used to capture the correlation between features to improve clustering performance. The IDBO algorithm is integrated with the DPC algorithm, and the F-Measure evaluation index is used to design the objective function so that the optimal value of the cut-off distance can be automatically selected. In order to evaluate the efficiency of the algorithm, three sets of artificially synthesized datasets and five sets of UCI standard datasets were chosen for studies. Experimental results show that the IDBO-MDDPC algorithm can automatically determine a better cut-off distance value and ensure higher clustering accuracy.
引用
收藏
页码:6179 / 6191
页数:13
相关论文
共 50 条
  • [31] A dual-optimization wind speed forecasting model based on deep learning and improved dung beetle optimization algorithm
    Li, Yanhui
    Sun, Kaixuan
    Yao, Qi
    Wang, Lin
    ENERGY, 2024, 286
  • [32] Parameter identification of PMSM based on dung beetle optimization algorithm
    Yang, Xiaoliang
    Cui, Yuyue
    Jia, Lianhua
    Sun, Zhihong
    Zhang, Peng
    Zhao, Jiane
    Wang, Rui
    ARCHIVES OF ELECTRICAL ENGINEERING, 2023, 72 (04) : 1055 - 1072
  • [33] Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization
    Li, Zhihao
    Xiao, Ping
    Pan, Jiabao
    Pei, Wenjun
    Lv, Aoning
    PLOS ONE, 2025, 20 (01):
  • [34] Multi-impulse pursuit-evasion game in GEO based on improved dung beetle optimization
    Guo, Yanning
    Li, Gaojian
    Yu, Yongbin
    CHINESE SPACE SCIENCE AND TECHNOLOGY, 2024, 44 (04) : 1 - 10
  • [35] Grinding process optimization considering carbon emissions, cost and time based on an improved dung beetle algorithm
    Lu, Qi
    Chen, Yonghao
    Zhang, Xuhui
    COMPUTERS & INDUSTRIAL ENGINEERING, 2024, 197
  • [36] Parameter Identification of PEMFC Model Using Improved Dung Beetle Optimization Algorithm
    Zhang, Jingfeng
    Sun, Yalu
    Dong, Haiying
    He, Xin
    ELECTRONICS, 2025, 14 (01):
  • [37] Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications
    Ye, Mingjun
    Zhou, Heng
    Yang, Haoyu
    Hu, Bin
    Wang, Xiong
    BIOMIMETICS, 2024, 9 (05)
  • [38] An Improved Dung Beetle Optimization Algorithm for High-Dimension Optimization and Its Engineering Applications
    Wang, Xu
    Kang, Hongwei
    Shen, Yong
    Sun, Xingping
    Chen, Qingyi
    SYMMETRY-BASEL, 2024, 16 (05):
  • [39] A fast density peak clustering based particle swarm optimizer for dynamic optimization
    Li, Fei
    Yue, Qiang
    Liu, Yuanchao
    Ouyang, Haibin
    Gu, Fangqing
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 236
  • [40] An Improved Density Peak Clustering Algorithm for Multi-Density Data
    Yin, Lifeng
    Wang, Yingfeng
    Chen, Huayue
    Deng, Wu
    SENSORS, 2022, 22 (22)