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A novel bidirectional clustering algorithm based on local density

Lookup NU author(s): Professor Zhiqiang Hu



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


With the widely application of cluster analysis, the number of clusters is gradually increasing, as is the difficulty in selecting the judgment indicators of cluster numbers. Also, small clusters are crucial to discovering the extreme characteristics of data samples, but current clustering algorithms focus mainly on analyzing large clusters. In this paper, a bidirectional clustering algorithm based on local density (BCALoD) is proposed. BCALoD establishes the connection between data points based on local density, can automatically determine the number of clusters, is more sensitive to small clusters, and can reduce the adjusted parameters to a minimum. On the basis of the robustness of cluster number to noise, a denoising method suitable for BCALoD is proposed. Different cutoff distance and cutoff density are assigned to each data cluster, which results in improved clustering performance. Clustering ability of BCALoD is verified by randomly generated datasets and city light satellite images.

Publication metadata

Author(s): Lyu B, Wu W, Hu Z

Publication type: Article

Publication status: Published

Journal: Scientific Reports

Year: 2021

Volume: 11

Online publication date: 09/07/2021

Acceptance date: 18/06/2021

Date deposited: 21/06/2021

ISSN (electronic): 2045-2322

Publisher: Springer Nature


DOI: 10.1038/s41598-021-93244-2


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