Key points are not available for this paper at this time.
In this age of ever-increasing data set sizes, especially in the natural sciences, visualisation becomes more and more important. Self-organizing maps have many features that make them attractive in this respect: they do not rely on distributional assumptions, can handle huge data sets with ease, and have shown their worth in a large number of applications. In this paper, we highlight the kohonen package for R, which implements self-organizing maps as well as some extensions for supervised pattern recognition and data fusion.
Building similarity graph...
Analyzing shared references across papers
Loading...
Ron Wehrens
Wageningen University & Research
L.M.C. Buydens
Radboud University Nijmegen
Journal of Statistical Software
Radboud University Nijmegen
Building similarity graph...
Analyzing shared references across papers
Loading...
Wehrens et al. (Mon,) studied this question.
synapsesocial.com/papers/69d9ad0c0d540cafc5836e14 — DOI: https://doi.org/10.18637/jss.v021.i05