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	A Novel Approximate Spectral Clustering Algorithm With Dense Cores and Density Peaks
	Cheng, Dongdong1; Huang, Jinlong1; Zhang, Sulan1; Zhang, Xiaohua2; Luo, Xin3,4,5
	2021-01-20
摘要	Spectral clustering is becoming more and more popular because it has good performance in discovering clusters with varying characteristics. However, it suffers from high computational cost, unstable clustering results and noises. This work presents a novel approximate spectral clustering based on dense cores and density peaks, called DCDP-ASC. It first finds a reduced data set by introducing the concept of dense cores; then defines a new distance based on the common neighborhood of dense cores and calculates geodesic distances between dense cores according to the new defined distance; after that constructs a decision graph with a parameter-free local density and geodesic distance for obtaining initial centers; finally calculates the similarity between dense cores with their new defined geodesic distance, employs normalized spectral clustering method to divide dense cores, and expands the result on dense cores to the whole data set by assigning each point to its representative. The results on some challenging data sets and the comparison of our algorithm with some other excellent methods demonstrate that the proposed method DCDP-ASC is more advantageous in identifying complex structured clusters containing a lot of noises.
关键词	Clustering algorithms Manifolds Matrix decomposition Sparse matrices Partitioning algorithms Approximation algorithms Level measurement Approximate spectral clustering common neighborhood-based distance dense cores density peaks geodesic distance
DOI	10.1109/TSMC.2021.3049490
发表期刊	IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
ISSN	2168-2216
页码	13
通讯作者	Huang, Jinlong(h.jinlong@yznu.edu.cn) ; Luo, Xin(luoxin21@cigit.ac.cn)
收录类别	SCI
WOS记录号	WOS:000733160600001
语种	英语