Knowledge Discovery by Accuracy Maximization

KODAMA algorithm is an unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. The algorithm was published by Cacciatore et al. 2014 . Addition functions was introduced by Cacciatore et al. 2017 to facilitate the identification of key features associated with the generated output and are easily interpretable for the user. Cross-validated techniques are also included in this package.


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install.packages("KODAMA")

1.5 by Stefano Cacciatore, 6 months ago


Browse source code at https://github.com/cran/KODAMA


Authors: Stefano Cacciatore , Leonardo Tenori , Claudio Luchinat , Phillip R. Bennett , and David A. MacIntyre


Documentation:   PDF Manual  


GPL (>= 2) license


Imports Rcpp

Depends on stats

Suggests rgl, knitr, rmarkdown

Linking to Rcpp, RcppArmadillo


See at CRAN