Variables Selection for Clustering and Classification

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.


Reference manual

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1.0 by Stefano Benati, 5 years ago

Browse source code at

Authors: Stefano Benati

Documentation:   PDF Manual  

GPL-2 license

Imports Rcpp

Depends on lpSolveAPI

Suggests clusterGeneration, mclust

Linking to Rcpp

See at CRAN