On K-means algorithm with the use of mahalanobis distances

Igor Melnykov, Volodymyr Melnykov

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)


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
Pages (from-to)88-95
Number of pages8
JournalStatistics and Probability Letters
Issue number1
Publication statusPublished - Jan 2014


  • Initialization
  • K-means algorithm
  • Mahalanobis distance

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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