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A survey on density-based clustering algorithms

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dc.contributor.authorLoh W.-K.-
dc.contributor.authorPark Y.-H.-
dc.date.available2021-02-22T10:55:04Z-
dc.date.created2020-09-02-
dc.date.issued2014-
dc.identifier.issn1876-1100-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/6172-
dc.description.abstractDensity-based clustering forms the clusters of densely gathered objects separated by sparse regions. In this paper, we survey the previous and recent density-based clustering algorithms. DBSCAN [6], OPTICS [1], and DENCLUE [5, 6] are previous representative density-based clustering algorithms. Several recent algorithms such as PDBSCAN [8], CUDA-DClust [3], and GSCAN [7] have been proposed to improve the performance of DBSCAN. They make the most of multi-core CPUs and GPUs. © Springer-Verlag Berlin Heidelberg 2014.-
dc.language영어-
dc.language.isoen-
dc.language.isoen-
dc.publisherSpringer Verlag-
dc.titleA survey on density-based clustering algorithms-
dc.typeArticle-
dc.contributor.affiliatedAuthorPark Y.-H.-
dc.identifier.doi10.1007/978-3-642-41671-2_98-
dc.identifier.scopusid2-s2.0-84958534116-
dc.identifier.bibliographicCitationLecture Notes in Electrical Engineering, v.280 LNEE, pp.775 - 780-
dc.relation.isPartOfLecture Notes in Electrical Engineering-
dc.citation.titleLecture Notes in Electrical Engineering-
dc.citation.volume280 LNEE-
dc.citation.startPage775-
dc.citation.endPage780-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusInformation technology-
dc.subject.keywordPlusProgram processors-
dc.subject.keywordPlusSurveys-
dc.subject.keywordPlusDensity-based Clustering-
dc.subject.keywordPlusDensity-based clustering algorithms-
dc.subject.keywordPlusMulti-core cpus-
dc.subject.keywordPlusClustering algorithms-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007%2F978-3-642-41671-2_98-
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