mkmeans: Mahalanobis K-means
In VBmix: Variational Bayesian Mixture Models

Description Usage Arguments Details Value Author(s) See Also Examples

K-means variant that uses a class-wise Mahalanobis metric. The implementation follows somewhat Lloyd's, with class-wise covariance computation step following that of centres.

1	mkmeans(dat, k, maxiter = 100, seeds = NULL, prior = 1)

`dat`	Matrix with n rows and d columns of n d-dimensional data elements to cluster.
`k`	Number of clusters in the output.
`maxiter`	Maximum number of iterations.
`seeds`	Optional indexes of initial centres taken in the input data. If NULL, uniform sampling is used.
`prior`	Prior population size used for regularizing components.

K-means is characterized by the use of identity as the metric. To remain close to this in spirit, each class-wise covariance matrix is normalized after computation so that is trace equals d. This avoids excessively unbalanced classes, while facilitating the case where the support of a given cluster is less than 2 - covariance cannot be computed in this case. Covariance then defaults to identity. Also to prevent degeneracies when 2 < cluster size < d, a regularization term proportional to sample data features is added to the covariance diagonal.

The returned value follows the GMM data structure (i.e., as returned by e.g. varbayes() and newGmm())