On K-means algorithm with the use of mahalanobis distances

Igor Melnykov, Volodymyr Melnykov

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)88-95
Number of pages8
JournalStatistics and Probability Letters
Volume84
Issue number1
DOIs
StatePublished - Jan 2014
Externally publishedYes

Bibliographical note

Funding Information:
This research was supported in part by the Seed Grant of the Corporate Fund “ Fund of Social Development ” of Nazarbayev University.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Initialization
  • K-means algorithm
  • Mahalanobis distance

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