Quantum algorithm for MMNG-based DBSCAN

DBSCAN is a famous density-based clustering algorithm that can discover clusters with arbitrary shapes without the minimal requirements of domain knowledge to determine the input parameters. However, DBSCAN is not suitable for databases with different local-density clusters and is also a very time-c...

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Detalles Bibliográficos
Autores principales: Xie, Xuming, Duan, Longzhen, Qiu, Taorong, Li, Junru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8324839/
https://www.ncbi.nlm.nih.gov/pubmed/34330983
http://dx.doi.org/10.1038/s41598-021-95156-7
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author Xie, Xuming
Duan, Longzhen
Qiu, Taorong
Li, Junru
author_facet Xie, Xuming
Duan, Longzhen
Qiu, Taorong
Li, Junru
author_sort Xie, Xuming
collection PubMed
description DBSCAN is a famous density-based clustering algorithm that can discover clusters with arbitrary shapes without the minimal requirements of domain knowledge to determine the input parameters. However, DBSCAN is not suitable for databases with different local-density clusters and is also a very time-consuming clustering algorithm. In this paper, we present a quantum mutual MinPts-nearest neighbor graph (MMNG)-based DBSCAN algorithm. The proposed algorithm performs better on databases with different local-density clusters. Furthermore, the proposed algorithm has a dramatic increase in speed compared to its classic counterpart.
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spelling pubmed-83248392021-08-02 Quantum algorithm for MMNG-based DBSCAN Xie, Xuming Duan, Longzhen Qiu, Taorong Li, Junru Sci Rep Article DBSCAN is a famous density-based clustering algorithm that can discover clusters with arbitrary shapes without the minimal requirements of domain knowledge to determine the input parameters. However, DBSCAN is not suitable for databases with different local-density clusters and is also a very time-consuming clustering algorithm. In this paper, we present a quantum mutual MinPts-nearest neighbor graph (MMNG)-based DBSCAN algorithm. The proposed algorithm performs better on databases with different local-density clusters. Furthermore, the proposed algorithm has a dramatic increase in speed compared to its classic counterpart. Nature Publishing Group UK 2021-07-30 /pmc/articles/PMC8324839/ /pubmed/34330983 http://dx.doi.org/10.1038/s41598-021-95156-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Xie, Xuming
Duan, Longzhen
Qiu, Taorong
Li, Junru
Quantum algorithm for MMNG-based DBSCAN
title Quantum algorithm for MMNG-based DBSCAN
title_full Quantum algorithm for MMNG-based DBSCAN
title_fullStr Quantum algorithm for MMNG-based DBSCAN
title_full_unstemmed Quantum algorithm for MMNG-based DBSCAN
title_short Quantum algorithm for MMNG-based DBSCAN
title_sort quantum algorithm for mmng-based dbscan
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8324839/
https://www.ncbi.nlm.nih.gov/pubmed/34330983
http://dx.doi.org/10.1038/s41598-021-95156-7
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AT lijunru quantumalgorithmformmngbaseddbscan

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