I clustered my dataset of several thousand first-order Markov chains into about 10 clusters.
Is there some recommended way how I can evaluate these clusters and find out what the items in the clusters share and how they differ from other clusters? So I can make statement like "Processes in cluster A tend to stay in state Y once they get there, which is not true for processes in other clusters."
The transition matrices of those Markov chains are too large to just "look and see". They are relatively sparse, if that can help.
My idea was to take all the transition matrices in a cluster, sum them and plot it as intensity in a picture (in a scale from 0 to 255). Is there something more "professional" I should try out?