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

dc.contributor.author	Melnykov, V.
dc.contributor.author	Melnykov, I.
dc.contributor.author	Michael, S.
dc.date.accessioned	2015-11-02T06:16:15Z
dc.date.available	2015-11-02T06:16:15Z
dc.date.issued	2014
dc.identifier.isbn	9786018046728
dc.identifier.uri	http://nur.nu.edu.kz/handle/123456789/651
dc.description.abstract	Cluster 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.language.iso	en	ru_RU
dc.publisher	Nazarbayev University	ru_RU
dc.subject	cluster analysis	ru_RU
dc.subject	negative equivalence	ru_RU
dc.subject	positive equivalence	ru_RU
dc.title	The use of positive and negative equivalence constraints in model-based clustering	ru_RU
dc.type	Abstract	ru_RU