Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1152774 | Statistics & Probability Letters | 2014 | 8 Pages |
Abstract
The KK-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
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Igor Melnykov, Volodymyr Melnykov,