Hierarchical clustering with deep Q-learning
  The reconstruction and analyzation of high energy particle physics data is
just as important as the analyzation of the structure in real world networks.
In a previous study it was explored how hierarchical clustering algorithms can
be combined with kt cluster algorithms to provide a more generic clusterization
method. Building on that, this paper explores the possibilities to involve deep
learning in the process of cluster computation, by applying reinforcement
learning techniques. The result is a model, that by learning on a modest
dataset of 10; 000 nodes during 70 epochs can reach 83; 77% precision in
predicting the appropriate clusters.
