Quantum algorithm for MMNG-based DBSCAN

Abstract 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 ve...

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Autores principales:	Xuming Xie, Longzhen Duan, Taorong Qiu, Junru Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5fbf1dc30afe45c7bafe2b97a72a99a0
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spelling	oai:doaj.org-article:5fbf1dc30afe45c7bafe2b97a72a99a02021-12-02T16:30:10ZQuantum algorithm for MMNG-based DBSCAN10.1038/s41598-021-95156-72045-2322https://doaj.org/article/5fbf1dc30afe45c7bafe2b97a72a99a02021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95156-7https://doaj.org/toc/2045-2322Abstract 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.Xuming XieLongzhen DuanTaorong QiuJunru LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xuming Xie Longzhen Duan Taorong Qiu Junru Li Quantum algorithm for MMNG-based DBSCAN
description	Abstract 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.
format	article
author	Xuming Xie Longzhen Duan Taorong Qiu Junru Li
author_facet	Xuming Xie Longzhen Duan Taorong Qiu Junru Li
author_sort	Xuming Xie
title	Quantum algorithm for MMNG-based DBSCAN
title_short	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_sort	quantum algorithm for mmng-based dbscan
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5fbf1dc30afe45c7bafe2b97a72a99a0
work_keys_str_mv	AT xumingxie quantumalgorithmformmngbaseddbscan AT longzhenduan quantumalgorithmformmngbaseddbscan AT taorongqiu quantumalgorithmformmngbaseddbscan AT junruli quantumalgorithmformmngbaseddbscan
_version_	1718383932701933568

Quantum algorithm for MMNG-based DBSCAN

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