Density peaks clustering algorithm based on shared nearest neighbor and second-order K nearest neighbor for manifold data
ZHAO Jia
CHEN Wei-chang
XIAO Ren-bin
PAN Jeng-Shyang
CUI Zhi-hua
WANG hui
Abstract：The density peaks clustering algorithm can deal with datasets quickly and efficiently without iteration.How-ever,it can sometimes wrongly select cluster centers and misallocate samples when processing manifold data.Therefore,this paper proposes the density peaks clustering algorithm based on shared nearest neighbor and second-order K nearest neighbor for manifold data(DPC-SKNN)algorithm.Firstly,the algorithm introduces reverse nearest neighbors and shares nearest neighbors to redefine local density,fully considering both local and global information of samples,making the al-gorithm easier to identify correct cluster centers.Secondly,the association relationship of the samples is divided into three types:K nearest neighbors,second-order K nearest neighbors,and non-nearest neighbors,and design allocation strategies for K-nearest neighbors to enhance similarity among samples within the same cluster,thereby improving sample allocation accuracy.DPC-SKNN is compared with eight algorithms on manifold and UCI datasets,and the experimental results show that the DPC-SKNN algorithm obtains good clustering results on all the above datasets.
Keywords：density peaks clusteringreverse nearest neighborshared nearest neighborsecond-order K nearest neigh-bormanifold data
Publication Date：2026-02-28
Online Publishing Date：2026-04-08(First online date of this platform, not the publication date of the document)
Pages：9( 388-396 )