Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 241200068-11.doi: 10.11896/jsjkx.241200068

• Big Data & Data Science • Previous Articles     Next Articles

Tensor Graph Diffusion Share Nearest Neighbor Density Peaks Clustering

LIU Qiaoming1,2,3, WEI Qianran2, LI Zhi2, WANG Jian4, LI Yuanfang2,3   

  1. 1 School of Artifical Intelligence,Henan University,Zhengzhou 450001,China
    2 Zhengzhou Research Institute,Harbin Institute of Technology,Zhengzhou 450001,China
    3 School of Medicine and Health,Harbin Institute of Technology,Harbin 150001,China
    4 School of Computer and Information Engineering,Henan University of Economics and Law,Zhengzhou 450001,China
  • Online:2025-11-15 Published:2025-11-10
  • Supported by:
    National Natural Science Foundation of China(62402145),China Postdoctoral Science Foundation under General Program(GZC20233467),Postdoctoral Fellowship Program of CPSF(2023M740932) and Natural Science Foundation of Henan Province,China(242300421705).

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Abstract: Density Peak Clustering(DPC) is based on the idea of density clustering.When dealing with high-dimensional data,the DPC algorithm has the problem of the “clustering” effect and “domino” effect in the process of similarity calculation and cluster distribution respectively,which limits the analysis efficiency of DPC in practical application.To solve these problems,a Tensor Graph Diffusion Share Nearest Neighbor Density Peaks Clustering(TGD-SNN-DPC) is proposed.Firstly,an adaptive tensor graph construction module is designed based on tensor graph theory to mine diverse local neighborhood information among data points.On this basis,an efficient tensor graph diffusion learning module is proposed and an efficient update strategy is introduced.On the premise of not increasing the computing burden of the model,this module is used to mine the global high-level topological information of the data,and the adjacency similarity information between samples with stronger robustness is obtained by using the above two modules.Finally,the adaptive shared neighborhood clustering module is designed.Based on the high-order adjacency matrix generated by tensor graph diffusion,the local density and relative distance based on shared neighbor information are introduced,and the adaptive neighborhood non-center sample allocation strategy is designed to improve the accuracy of the model matrix.Experimental results on 6 synthetic datasets and 12 real UCI datasets show that TGD-SNN-DPC algorithm outperforms the benchmark algorithm in clustering.

Key words: Clustering analysis, Density peak clustering, Tensor graph, Diffusion process, Shared nearest neighbors

CLC Number: 

  • TP391.41
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