CLUSTERING OF OBJECTS USING THE DBSCAN ALGORITHM

dc.contributor.authorShavkat Fayzullayevich Madraximov
dc.contributor.authorSherzod Dilmurodovich Dilmurodov
dc.contributor.authorFayzulla Yusupovich Shodiyev
dc.contributor.authorMunisa Davronova
dc.date.accessioned2025-12-28T10:50:12Z
dc.date.issued2025-05-17
dc.description.abstractThis article discusses the solution to the problem of identifying complex hidden relationships between varieties by clustering using the DBSCAN algorithm, dividing the features of soft wheat varieties into groups (reflecting the growth and development of the wheat plant, components of fertility and fertility, reflecting the quality of the grain).
dc.formatapplication/pdf
dc.identifier.urihttps://usajournals.org/index.php/2/article/view/105
dc.identifier.urihttps://asianeducationindex.com/handle/123456789/4223
dc.language.isoeng
dc.publisherModern American Journals
dc.relationhttps://usajournals.org/index.php/2/article/view/105/134
dc.rightshttps://creativecommons.org/licenses/by/4.0
dc.sourceModern American Journal of Engineering, Technology, and Innovation; Vol. 1 No. 2 (2025); 64-72
dc.source3067-7939
dc.subjectDBSCAN, legality, K-means,  − neighboured, cluster, root object, boundary object, noise object, MinPts.
dc.titleCLUSTERING OF OBJECTS USING THE DBSCAN ALGORITHM
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typePeer-reviewed Article

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