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dc.contributor.authorMladenović, Nenaden
dc.contributor.authorHansen, Pierreen
dc.contributor.authorBrimberg, Jacken
dc.date.accessioned2020-05-02T16:42:04Z-
dc.date.available2020-05-02T16:42:04Z-
dc.date.issued2013-06-01en
dc.identifier.issn1435-246Xen
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/2470-
dc.description.abstractSequential clustering aims at determining homogeneous and/or well-separated clusters within a given set of entities, one at a time, until no more such clusters can be found. We consider a bi-criterion sequential clustering problem in which the radius of a cluster (or maximum dissimilarity between an entity chosen as center and any other entity of the cluster) is chosen as a homogeneity criterion and the split of a cluster (or minimum dissimilarity between an entity in the cluster and one outside of it) is chosen as a separation criterion. An O(N 3) algorithm is proposed for determining radii and splits of all efficient clusters, which leads to an O(N 4) algorithm for bi-criterion sequential clustering with radius and split as criteria. This algorithm is illustrated on the well known Ruspini data set.en
dc.publisherSpringer Link-
dc.relation.ispartofCentral European Journal of Operations Researchen
dc.subjectClustering | Efficient cluster | Radius | Sequential | Spliten
dc.titleSequential clustering with radius and split criteriaen
dc.typeArticleen
dc.identifier.doi10.1007/s10100-012-0258-3en
dc.identifier.scopus2-s2.0-84878017125en
dc.relation.firstpage95en
dc.relation.lastpage115en
dc.relation.issueSUPPL1en
dc.relation.volume21en
dc.description.rankM23-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
crisitem.author.orcid0000-0001-6655-0409-
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