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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| Auteurs principaux: | , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/5fbf1dc30afe45c7bafe2b97a72a99a0 |
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| Résumé: | 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. |
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