# R-MDPC: Fast main density peak clustering within relevant regions via a robust decision graph
which is published in Pattern Recognition, 2024. Doi:10.1016/j.patcog.2024.110458
Abstract: Although density Peak Clustering (DPC) can easily locate cluster centers by detecting density peaks in its decision graph, its allocation strategy may unadvisedly associate irrelevant points, its decision graph may mislead the cluster center selection, and its high computational complexity O(n^2) shies itself away from large-scale data. Herein, a Fast Main Density Peak Clustering Within Relevant Regions Via A Robust Decision Graph (R-MDPC) is proposed. R-MDPC assigns points within the relevant regions to avoid the association of irrelevant points. With the removal of regional differences and the attenuation of satellite peaks, a robust decision graph is obtained. Moreover, based on the kNN distance of data points, R-MDPC is believed to be suitable for large-scale data. Experimental results demonstrated the high robustness of R-MDPC’s decision graph in identifying cluster centers, and its outstanding performance and fast running speed in recognizing complex-shaped clusters.
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R-MDPC:通过鲁棒决策图在相关区域内快速主密度峰值聚类matlab代码.zip
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R-MDPC:通过鲁棒决策图在相关区域内快速主密度峰值聚类matlab代码.zip (22个子文件)
R-MDPC:通过鲁棒决策图在相关区域内快速主密度峰值聚类matlab代码
R-MDPC-main
data
atom.mat 18KB
s3.mat 32KB
R15.mat 4KB
D31.mat 32KB
Flame.mat 4KB
AggFlame.mat 8KB
chainlink.mat 23KB
jain.mat 2KB
Agg.mat 3KB
README.md 1KB
code
GetNmi.m 1KB
GetF_Score.m 827B
Library.m 9KB
main.m 1KB
GetAmi.m 5KB
realcenter.m 395B
R_MDPC.m 7KB
resultshow.m 3KB
Evaluation.m 251B
GetDGCI.m 1KB
ourcolormap.mat 2KB
PRE_REC.m 389B
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