Highdimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen VP (2019) Numero: a statistical framework to define multivariable subgroups in complex populationbased datasets, Int J Epidemiology, 48:36937, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a selforganizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.
Package details 


Author  Song Gao [aut], Stefan Mutter [aut], Aaron E. Casey [aut], VillePetteri Makinen [aut, cre] 
Maintainer  VillePetteri Makinen <vpmakine@gmail.com> 
License  GPL (>= 2) 
Version  1.9.3 
Package repository  View on CRAN 
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