An Effective Clustering Algorithm Using Adaptive Neighborhood and Border Peeling Method

Traditional clustering methods often cannot avoid the problem of selecting neighborhood parameters and the number of clusters, and the optimal selection of these parameters varies among different shapes of data, which requires prior knowledge. To address the above parameter selection problem, we pro...

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Autores principales:	Ji Feng, Bokai Zhang, Ruisheng Ran, Wanli Zhang, Degang Yang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/f3c3e7aa0712468ca13c2a8aa79add12
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spelling	oai:doaj.org-article:f3c3e7aa0712468ca13c2a8aa79add122021-11-22T01:09:40ZAn Effective Clustering Algorithm Using Adaptive Neighborhood and Border Peeling Method1687-527310.1155/2021/6785580https://doaj.org/article/f3c3e7aa0712468ca13c2a8aa79add122021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6785580https://doaj.org/toc/1687-5273Traditional clustering methods often cannot avoid the problem of selecting neighborhood parameters and the number of clusters, and the optimal selection of these parameters varies among different shapes of data, which requires prior knowledge. To address the above parameter selection problem, we propose an effective clustering algorithm based on adaptive neighborhood, which can obtain satisfactory clustering results without setting the neighborhood parameters and the number of clusters. The core idea of the algorithm is to first iterate adaptively to a logarithmic stable state and obtain neighborhood information according to the distribution characteristics of the dataset, and then mark and peel the boundary points according to this neighborhood information, and finally cluster the data clusters with the core points as the centers. We have conducted extensive comparative experiments on datasets of different sizes and different distributions and achieved satisfactory experimental results.Ji FengBokai ZhangRuisheng RanWanli ZhangDegang YangHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Ji Feng Bokai Zhang Ruisheng Ran Wanli Zhang Degang Yang An Effective Clustering Algorithm Using Adaptive Neighborhood and Border Peeling Method
description	Traditional clustering methods often cannot avoid the problem of selecting neighborhood parameters and the number of clusters, and the optimal selection of these parameters varies among different shapes of data, which requires prior knowledge. To address the above parameter selection problem, we propose an effective clustering algorithm based on adaptive neighborhood, which can obtain satisfactory clustering results without setting the neighborhood parameters and the number of clusters. The core idea of the algorithm is to first iterate adaptively to a logarithmic stable state and obtain neighborhood information according to the distribution characteristics of the dataset, and then mark and peel the boundary points according to this neighborhood information, and finally cluster the data clusters with the core points as the centers. We have conducted extensive comparative experiments on datasets of different sizes and different distributions and achieved satisfactory experimental results.
format	article
author	Ji Feng Bokai Zhang Ruisheng Ran Wanli Zhang Degang Yang
author_facet	Ji Feng Bokai Zhang Ruisheng Ran Wanli Zhang Degang Yang
author_sort	Ji Feng
title	An Effective Clustering Algorithm Using Adaptive Neighborhood and Border Peeling Method
title_short	An Effective Clustering Algorithm Using Adaptive Neighborhood and Border Peeling Method
title_full	An Effective Clustering Algorithm Using Adaptive Neighborhood and Border Peeling Method
title_fullStr	An Effective Clustering Algorithm Using Adaptive Neighborhood and Border Peeling Method
title_full_unstemmed	An Effective Clustering Algorithm Using Adaptive Neighborhood and Border Peeling Method
title_sort	effective clustering algorithm using adaptive neighborhood and border peeling method
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/f3c3e7aa0712468ca13c2a8aa79add12
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An Effective Clustering Algorithm Using Adaptive Neighborhood and Border Peeling Method

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