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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/32556
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dc.contributor.authorParkhimenka, U.-
dc.contributor.authorTatur, M.-
dc.contributor.authorKhandogina, O.-
dc.date.accessioned2018-07-12T12:09:53Z-
dc.date.available2018-07-12T12:09:53Z-
dc.date.issued2017-
dc.identifier.citationParkhimenka, U. Unsupervised ranking of clients: machine learning approach to define a "good customer" / U. Parkhimenka, M. Tatur, O. Khandogina // Central European Researchers Journal. – 2017. - Volume 3, Issue 2. - Pp. 10 - 15.ru_RU
dc.identifier.urihttps://libeldoc.bsuir.by/handle/123456789/32556-
dc.description.abstractRanking of clientsis a natural problem for every business. Though usually it can be solved by common sense and intuition of managers, in the case of a big business entity (e.g. global online stores), the problem becomes more complicated with obvious obstacles in derivation of fast and accurate solution. This article deals with the clients ranking problem using machine learning methodology.ru_RU
dc.language.isoenru_RU
dc.publisherFaculty of Management Science and Informaticsru_RU
dc.subjectпубликации ученыхru_RU
dc.subjectloyalty ladderru_RU
dc.subjectrankingru_RU
dc.subjectecommerceru_RU
dc.subjectautomatic marketing decision-makingru_RU
dc.subjectmachine learningru_RU
dc.subjectdata mining & knowledge discoveryru_RU
dc.subjectlatent variable analysisru_RU
dc.titleUnsupervised ranking of clients: machine learning approach to define a "good customer"ru_RU
dc.typeСтатьяru_RU
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