Adaptive Density Subgraph Clustering

Density peak clustering (DPC) has garnered growing interest over recent decades due to its capability to identify clusters with diverse shapes and its resilience to the presence of noisy data. Most DPC-based methods exhibit high computational complexity. One approach to mitigate this issue involves...

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Veröffentlicht in:	IEEE transactions on computational social systems Jg. 11; H. 4; S. 5468 - 5482
Hauptverfasser:	Jia, Hongjie, Wu, Yuhao, Mao, Qirong, Li, Yang, Song, Heping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.08.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive algorithms Adaptive clustering Algorithm design and theory Cluster analysis cluster self-ensemble Clustering Clustering algorithms Data analysis Datasets Density density clustering Density measurement density subgraph Estimation Graph theory Merging Outliers (statistics) Sensitivity Shape measurement Task analysis Time complexity
ISSN:	2329-924X, 2373-7476
Online-Zugang:	Volltext
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