A Grid-Based Density Peaks Clustering Algorithm

This work focuses on the improvement of density peaks clustering (DPC, also known as clustering by fast search and find of density peaks, FSFDP) by the introduction of the grid clustering. A grid division is used to divide the data space into grid cells. The global characteristic of grid cell replac...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on industrial informatics Ročník 19; číslo 4; s. 1 - 8
Hlavní autoři: Fang, Xintong, Xu, Zhen, Ji, Haifeng, Wang, Baoliang, Huang, Zhiyao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Clustering
Clustering algorithms
Clustering methods
Computational complexity
Data points
Density
Distributed databases
efficiency
grid
machine learning
Manuals
Resource management
ISSN:1551-3203, 1941-0050
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This work focuses on the improvement of density peaks clustering (DPC, also known as clustering by fast search and find of density peaks, FSFDP) by the introduction of the grid clustering. A grid division is used to divide the data space into grid cells. The global characteristic of grid cell replaces the characteristic of the data points. The local density of DPC is replaced by the density of the grid cell. The cut-off distance of DPC is no longer needed. Then, the cluster centers are determined adaptively based on the quantities. Finally, a two-stage allocation strategy (neighborhood searching and border grid cell assigning) is introduced to obtain the final clustering results. Six datasets are used to experimentally verify the effectiveness and performance of the proposed algorithm. Experimental results show that the improvement of DPC is successful. Compared with DPC, the proposed algorithm is more efficient with less manual intervention.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3203721