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.


Reference manual

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1.5 by Stefano Cacciatore, a year ago

Browse source code at

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