On K-means algorithm with the use of Mahalanobis distances

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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Year of publication:	2014
Authors:	Melnykov, Igor ; Melnykov, Volodymyr
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 84.2014, C, p. 88-95
Publisher:	Elsevier
Subject:	K-means algorithm \| Mahalanobis distance \| Initialization

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010718814