Laplacian Centrality Peaks Clustering Based on Potential Entropy

Laplacian Centrality Peaks Clustering Based on Potential Entropy

The clustering analysis is an important unsupervised learning algorithm in data mining, which has a wide range of applications in the field of pattern recognition, image processing, and so on. The existing clustering algorithms generally need one or more pre-setting parameters, these parameters lack...

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Bibliographic Details
Main Authors: Xu-Hua Yang, Lin-Bo Jin, Wei Ye, Jie Xiao, Duan Zhang, Xin-Li Xu
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
DBSCAN
density-reachable
Laplacian centrality
parameter-free
peak clustering
potential entropy
Online Access:https://ieeexplore.ieee.org/document/8468315/

Internet

https://ieeexplore.ieee.org/document/8468315/

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