The use of positive and negative equivalence constraints in model-based clustering

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dc.contributor.authorMelnykov, V.
dc.contributor.authorMelnykov, I.
dc.contributor.authorMichael, S.
dc.date.accessioned2015-11-02T06:16:15Z
dc.date.available2015-11-02T06:16:15Z
dc.date.issued2014
dc.description.abstractCluster analysis is a popular technique in statistics and computer science with the objective to group similar observations into relatively distinct groups known as clusters. Semi-supervised model-based clustering assumes that some additional information about group memberships is available.ru_RU
dc.identifier.isbn9786018046728
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/651
dc.language.isoenru_RU
dc.publisherNazarbayev Universityru_RU
dc.subjectcluster analysisru_RU
dc.subjectnegative equivalenceru_RU
dc.subjectpositive equivalenceru_RU
dc.titleThe use of positive and negative equivalence constraints in model-based clusteringru_RU
dc.typeAbstractru_RU

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