  In magnetospheric missions, burst mode data sampling should be triggered in
the presence of processes of scientific or operational interest. We present an
unsupervised classification method for magnetospheric regions, that could
constitute the first-step of a multi-step method for the automatic
identification of magnetospheric processes of interest. Our method is based on
Self Organizing Maps (SOMs), and we test it preliminarily on data points from
global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The
dimensionality of the data is reduced with Principal Component Analysis before
classification. The classification relies exclusively on local plasma
properties at the selected data points, without information on their
neighborhood or on their temporal evolution. We classify the SOM nodes into an
automatically selected number of classes, and we obtain clusters that map to
well defined magnetospheric regions. We validate our classification results by
plotting the classified data in the simulated space and by comparing with
K-means classification. For the sake of result interpretability, we examine the
SOM feature maps (magnetospheric variables are called features in the context
of classification), and we use them to unlock information on the clusters. We
repeat the classification experiments using different sets of features, we
quantitatively compare different classification results, and we obtain insights
on which magnetospheric variables make more effective features for unsupervised
classification.
