On K-means algorithm with the use of Mahalanobis distances

By Igor Melnykov, Volodymyr Melnykov. 2014

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How much can k-means be improved by using better initialization and repeats? (2019, methodology): “In these cases, if natural clusters are wanted, a better clustering result could be achieved by using an objective function based on Mahalanobis distance [42] or Gaussian mixture model [43] instead of SSE.”
Weed detection by UAV: simulation of the impact of spectral mixing in multispectral images (2017, methodology): “The classification method used in this work is based on a K-means algorithm using a Mahalanobis distance (Melnykov and Melnykov 2014).”