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...

Täydet tiedot

Bibliografiset tiedot
Päätekijät: Yisen Lin, Xinlun Zhang, Lei Liu, Huichen Qu
Aineistotyyppi: Artikkeli
Kieli:English
Julkaistu: IEEE 2022-01-01
Sarja:IEEE Access
Aiheet:
Clustering
region density
mutual neighbors
dimensionality reduction
arbitrary shapes
pattern recognition
Linkit:https://ieeexplore.ieee.org/document/9987490/

Internet

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

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