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) |
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Pages (from-to) | 88-95 |
Number of pages | 8 |
Journal | Statistics and Probability Letters |
Volume | 84 |
Issue number | 1 |
DOIs | |
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