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...

Полное описание

Библиографические подробности
Главные авторы:	Yisen Lin, Xinlun Zhang, Lei Liu, Huichen Qu
Формат:	Статья
Язык:	English
Опубликовано:	IEEE 2022-01-01
Серии:	IEEE Access
Предметы:	Clustering region density mutual neighbors dimensionality reduction arbitrary shapes pattern recognition
Online-ссылка:	https://ieeexplore.ieee.org/document/9987490/

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