DEDIC: Density Estimation Clustering Method Using Directly Interconnected Cores

DEDIC: Density Estimation Clustering Method Using Directly Interconnected Cores

Numerous clustering algorithms become invalid when data classes are unbalanced and close to each other, or clusters are arbitrary shapes and have different densities. A novel region density based clustering algorithm, DEDIC, is proposed to meet this challenge. It is based on recognizing dense region...

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מידע ביבליוגרפי
Main Authors: Yisen Lin, Xinlun Zhang, Lei Liu, Huichen Qu
פורמט: Article
שפה:English
יצא לאור: IEEE 2022-01-01
סדרה:IEEE Access
נושאים:
Clustering
region density
mutual neighbors
dimensionality reduction
arbitrary shapes
pattern recognition
גישה מקוונת:https://ieeexplore.ieee.org/document/9987490/

אינטרנט

https://ieeexplore.ieee.org/document/9987490/

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