  The growing amount of data produced by simulations and observations of space
physics processes encourages the use of methods rooted in Machine Learning for
data analysis and physical discovery. We apply a clustering method based on
Self-Organizing Maps (SOM) to fully kinetic simulations of plasmoid
instability, with the aim of assessing its suitability as a reliable analysis
tool for both simulated and observed data. We obtain clusters that map well, a
posteriori, to our knowledge of the process: the clusters clearly identify the
inflow region, the inner plasmoid region, the separatrices, and regions
associated with plasmoid merging. SOM-specific analysis tools, such as feature
maps and Unified Distance Matrix, provide one with valuable insights into both
the physics at work and specific spatial regions of interest. The method
appears as a promising option for the analysis of data, both from simulations
and from observations, and could also potentially be used to trigger the switch
to different simulation models or resolution in coupled codes for space
simulations.
