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.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.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.language.iso | en | en_US |
| dc.publisher | Statistics & Probability Letters | en_US |
| dc.relation.ispartof | Statistics & Probability Letters | |
| dc.rights.license | Copyright © 2013 Elsevier B.V. All rights reserved. | |
| 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 |
| elsevier.aggregationtype | Journal | |
| elsevier.coverdate | 2014-01-01 | |
| elsevier.coverdisplaydate | January 2014 | |
| elsevier.endingpage | 95 | |
| 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.openaccess | 0 | |
| elsevier.openaccessarticle | false | |
| elsevier.openarchivearticle | false | |
| elsevier.startingpage | 88 | |
| 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.volume | 84 | |
| workflow.import.source | science |