[1]ZHAO Jia,MA Qing,XIAO Renbin,et al.Density peaks clustering based on shared nearest neighbor for manifold datasets[J].CAAI Transactions on Intelligent Systems,2023,18(4):719-730.[doi:10.11992/tis.202209026]
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Density peaks clustering based on shared nearest neighbor for manifold datasets

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 4 Page number: 719-730 Column: 学术论文—机器学习 Public date: 2023-07-15
Title:
Density peaks clustering based on shared nearest neighbor for manifold datasets
Author(s):
ZHAO Jia1;  MA Qing1;  XIAO Renbin2;  PAN Zhengxiang3;  HAN Longzhe1
1. School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China;
2. Institute of artificial intelligence and automation, Huazhong University of science and technology, Wuhan 430074, China;
3. Institute of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Keywords:
density peaks; clustering analysis; manifold data; K nearest neighbor; shared nearest neighbor; manifold data; sample similarity; data mining; image processing
CLC:
TP301.6
DOI:
10.11992/tis.202209026
Abstract:
Manifold data consists of some arc-shaped or ring-shaped clusters, which are characterized by a large distance between samples of the same cluster. Density peaks clustering (DPC) algorithm cannot effectively identify the cluster centers of the manifold clusters and is prone to the problem of continuous misallocation of samples when allocating the remaining samples. To solve these problems, density peaks clustering based on shared nearest neighbor (DPC-SNN) algorithm for manifold data is proposed in this paper. A sample similarity definition based on shared nearest neighbor is proposed to make the similarity between samples of the same manifold cluster as high as possible; Then, the local density is defined based on the above similarity without ignoring the density contribution of samples farther from the cluster centers, which can better distinguish the cluster centers from other samples of manifold cluster; And then, the remaining samples are allocated according to the similarity of samples to avoid continuous misallocation of samples. The comparative experimental results between DPC-SNN and other algorithms of DPC, FKNN-DPC, FNDPC, DPCSA and IDPC-FA show that DPC-SNN can effectively find the cluster centers of manifold data and accurately complete clustering, and has a good clustering effect on real and faces datasets.
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