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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.
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
URL: https://doi.org/10.1038/s41598-021-93244-2
DOI: 10.1038/s41598-021-93244-2
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