Density-based clustering validation: Difference between revisions

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Line 31: }}</ref> It utilizes density connectivity principles to quantify clustering structures, making it especially effective at detecting arbitrarily shaped clusters in concave datasets, where traditional metrics may be less reliable. The DBCV index has been employed infor ~~bioinformatics~~clustering analysis in bioinformatics,<ref name="Di Giovanni">{{Citation \| last= Di Giovanni \| first= Daniele Line 44: \| pmid= 36833240 \| pmc= 9956345 }}</ref> ecology ~~analysis~~,<ref name="Poutaraud">{{Citation \| last= Poutaraud \| first= Joachim Line 54: \| publisher = Elsevier \| doi = 10.1016/j.ecoinf.2024.102687 \| doi-access= free▼ ~~\| url = https://www.sciencedirect.com/science/article/pii/S1574954124002292~~ }}</ref> techno-~~economic analysis~~economy,<ref name="Shim">{{Citation▼ ▲\| doi-access= free ▲ }}</ref> techno-economic analysis,<ref name="Shim">{{Citation \| last= Shim \| first= Jaehyun Line 69 ⟶ 68: \| url = https://www.sciencedirect.com/science/article/abs/pii/S019689042201189X \| url-access= subscription }}</ref> and health informatics ~~analysis~~<ref name="Martinez">{{Citation \| last= Martínez \| first= Rubén Yáñez Line 80 ⟶ 79: \| publisher = Elsevier \| doi = 10.1016/j.ipm.2023.103294 \| doi-access= free▼ ~~\| url = https://www.sciencedirect.com/science/article/pii/S0306457323000316~~ }}</ref> ▲\| doi-access= free <ref>{{cite journal \| }}</ref> as well as in numerous other fields.<ref name="Beer">{{cite arXiv \|mode=cs2▼ author= Chicco D. \| author2= Oneto L. \| author3= Cangelosi D. \| title = DBSCAN and DBCV application to open medical records heterogeneous data for identifying clinically significant clusters of patients with neuroblastoma \| journal = BioData Mining \| volume = 18 \| issue = 40 \| date = 2025 \| page = 1-17 \| doi = 10.1186/s13040-025-00455-8 \| doi-access=free\| ▲ pmc = 12164137 }}</ref>, as well as in numerous other fields.<ref name="Beer">{{cite arXiv \|mode=cs2 \| last= Beer \| first= Anna Line 179 ⟶ 190: \| isbn = 978-1-61197-344-0 \| url = https://www.dbs.ifi.lmu.de/~zimek/publications/SDM2014/DBCV.pdf \| doi-access=free }} {{Citation == Implementations ==▼ \| last1 = Chicco \| first1 = Davide \| last2 = Sabino \| first2 = Giuseppe \| last3 = Oneto \| first3 = Luca \| last4 = Jurman \| first4 = Giuseppe \| chapter = The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters \| year = 2025 \| title = PeerJ Computer Science \| doi = 10.7717/peerj-cs.3095 \| pages = 1-37 \| publisher = PeerJ Inc. \| url = https://doi.org/10.7717/peerj-cs.3095 \| doi-access=free }} ▲== Implementations == [https://github.com/christopherjenness/DBCV Python DBCV Implementation by Christopher Jennes] * [https://github.com/FelSiq/DBCV Python DBCV Implementation by Felipe Alves Siqueira] * [https://doi.org/10.32614/cran.package.dbcvindex R DBCV Implementation by Pablo Andretta Jaskowiak]