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# On K-means algorithm with the use of Mahalanobis distances

 dc.contributor.author Melnykov, Igor dc.contributor.author Melnykov, Volodymyr dc.creator Igor, Melnykov dc.date.accessioned 2017-12-14T04:51:53Z dc.date.available 2017-12-14T04:51:53Z dc.date.issued 2014-01-01 dc.identifier DOI:10.1016/j.spl.2013.09.026 dc.identifier.citation Igor Melnykov, Volodymyr Melnykov, On K-means algorithm with the use of Mahalanobis distances, In Statistics & Probability Letters, Volume 84, 2014, Pages 88-95 en_US dc.identifier.issn 01677152 dc.identifier.uri https://www.sciencedirect.com/science/article/pii/S0167715213003246 dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/2887 dc.description.abstract Abstract The K-means algorithm is commonly used with the Euclidean metric. While the use of Mahalanobis distances seems to be a straightforward extension of the algorithm, the initial estimation of covariance matrices can be complicated. We propose a novel approach for initializing covariance matrices. en_US dc.language.iso en en_US dc.publisher Statistics & Probability Letters en_US dc.relation.ispartof Statistics & Probability Letters dc.subject K-means algorithm en_US dc.subject Mahalanobis distance en_US dc.subject Initialization en_US dc.title On K-means algorithm with the use of Mahalanobis distances en_US dc.type Article en_US dc.rights.license Copyright © 2013 Elsevier B.V. All rights reserved. elsevier.identifier.doi 10.1016/j.spl.2013.09.026 elsevier.identifier.eid 1-s2.0-S0167715213003246 elsevier.identifier.pii S0167-7152(13)00324-6 elsevier.identifier.scopusid 84885983328 elsevier.volume 84 elsevier.coverdate 2014-01-01 elsevier.coverdisplaydate January 2014 elsevier.startingpage 88 elsevier.endingpage 95 elsevier.openaccess 0 elsevier.openaccessarticle false elsevier.openarchivearticle false elsevier.teaser The K-means algorithm is commonly used with the Euclidean metric. While the use of Mahalanobis distances seems to be a straightforward extension of the algorithm, the initial estimation of covariance... elsevier.aggregationtype Journal workflow.import.source science
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