A novel bidirectional clustering algorithm based on local density

Abstract 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...

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Autores principales:	Baicheng Lyu, Wenhua Wu, Zhiqiang Hu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5e985accec854db2a5b03f60d72861a4
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id	oai:doaj.org-article:5e985accec854db2a5b03f60d72861a4
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spelling	oai:doaj.org-article:5e985accec854db2a5b03f60d72861a42021-12-02T16:15:07ZA novel bidirectional clustering algorithm based on local density10.1038/s41598-021-93244-22045-2322https://doaj.org/article/5e985accec854db2a5b03f60d72861a42021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93244-2https://doaj.org/toc/2045-2322Abstract 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.Baicheng LyuWenhua WuZhiqiang HuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Baicheng Lyu Wenhua Wu Zhiqiang Hu A novel bidirectional clustering algorithm based on local density
description	Abstract 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.
format	article
author	Baicheng Lyu Wenhua Wu Zhiqiang Hu
author_facet	Baicheng Lyu Wenhua Wu Zhiqiang Hu
author_sort	Baicheng Lyu
title	A novel bidirectional clustering algorithm based on local density
title_short	A novel bidirectional clustering algorithm based on local density
title_full	A novel bidirectional clustering algorithm based on local density
title_fullStr	A novel bidirectional clustering algorithm based on local density
title_full_unstemmed	A novel bidirectional clustering algorithm based on local density
title_sort	novel bidirectional clustering algorithm based on local density
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5e985accec854db2a5b03f60d72861a4
work_keys_str_mv	AT baichenglyu anovelbidirectionalclusteringalgorithmbasedonlocaldensity AT wenhuawu anovelbidirectionalclusteringalgorithmbasedonlocaldensity AT zhiqianghu anovelbidirectionalclusteringalgorithmbasedonlocaldensity AT baichenglyu novelbidirectionalclusteringalgorithmbasedonlocaldensity AT wenhuawu novelbidirectionalclusteringalgorithmbasedonlocaldensity AT zhiqianghu novelbidirectionalclusteringalgorithmbasedonlocaldensity
_version_	1718384292995792896

A novel bidirectional clustering algorithm based on local density

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