On K-means algorithm with the use of mahalanobis distances

Igor Melnykov; Volodymyr Melnykov

doi:10.1016/j.spl.2013.09.026

On K-means algorithm with the use of mahalanobis distances

Igor Melnykov, Volodymyr Melnykov

Research output: Contribution to journal › Article › peer-review

50 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 language	English (US)
Pages (from-to)	88-95
Number of pages	8
Journal	Statistics and Probability Letters
Volume	84
Issue number	1
DOIs	https://doi.org/10.1016/j.spl.2013.09.026
State	Published - Jan 2014
Externally published	Yes

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.

Keywords

Initialization
K-means algorithm
Mahalanobis distance

Publisher link

10.1016/j.spl.2013.09.026

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title = "On K-means algorithm with the use of mahalanobis distances",

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.",

keywords = "Initialization, K-means algorithm, Mahalanobis distance",

author = "Igor Melnykov and Volodymyr Melnykov",

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

year = "2014",

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AU - Melnykov, Volodymyr

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PY - 2014/1

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N2 - 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.

AB - 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.

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KW - K-means algorithm

KW - Mahalanobis distance

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DO - 10.1016/j.spl.2013.09.026

M3 - Article

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JO - Statistics and Probability Letters

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On K-means algorithm with the use of mahalanobis distances

Abstract

Bibliographical note

Keywords

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