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.
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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())
labels |
Cluster labels taking values in 1..k |
w |
Numeric vector of cluster weights |
mean |
List of mean vectors |
cov |
List of covariance matrices |
P. Bruneau
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