For a given data matrix A and cluster centers/prototypes collected in the matrix P, the functions described here select a subset of statistic variables Q that mostly explains/justifies P as prototypes. The functions are useful to reduce the data dimension for classification and to discard masking variables for clustering.
|Maintainer||Stefano Benati <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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